Federated Learning

Research on Federated Learning

Research papers include:

• Caelin Kaplan, Tareq Si Salem, Angelo Rodio, Chuan Xu, Giovanni Neglia, 7 May 2024, Federated Learning for Cooperative Inference Systems: The Case of Early Exit Networks, https://arxiv.org/abs/2405.04249
• Mengwei Xu, Wangsong Yin, Dongqi Cai, Rongjie Yi, Daliang Xu, Qipeng Wang, Bingyang Wu, Yihao Zhao, Chen Yang, Shihe Wang, Qiyang Zhang, Zhenyan Lu, Li Zhang, Shangguang Wang, Yuanchun Li, Yunxin Liu, Xin Jin, Xuanzhe Liu, 16 Jan 2024, A Survey of Resource-efficient LLM and Multimodal Foundation Models, https://arxiv.org/abs/2401.08092 Project: https://github.com/UbiquitousLearning/Efficient_Foundation_Model_Survey (Broad survey with many optimizations including this topic.)
• Mohamed Nabih Ali, Daniele Falavigna, Alessio Brutti, 2024, Fed-EE: Federating Heterogeneous ASR Models using Early-Exit Architectures, PDF: https://cris.fbk.eu/bitstream/11582/343747/1/paper_49.pdf
• H Woisetschläger, A Isenko, S Wang, R Mayer, 2023, Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly, https://arxiv.org/abs/2310.03150
• Lorenzo Sani, Alex Iacob, Zeyu Cao, Bill Marino, Yan Gao, Tomas Paulik, Wanru Zhao, William F. Shen, Preslav Aleksandrov, Xinchi Qiu, Nicholas D. Lane, 19 Jul 2024 (v2), The Future of Large Language Model Pre-training is Federated, https://arxiv.org/abs/2405.10853
• Jaxpruner: A Concise Library for Sparsity Research, Joo Hyung Lee, Wonpyo Park, Nicole Elyse Mitchell, Jonathan Pilault, Johan Samir Obando Ceron, Han-Byul Kim, Namhoon Lee, Elias Frantar, Yun Long, Amir Yazdanbakhsh, Woohyun Han, Shivani Agrawal, Suvinay Subramanian, Xin Wang, Sheng-Chun Kao, Xingyao Zhang, Trevor Gale, Aart J.C. Bik, Milen Ferev, Zhonglin Han, Hong-Seok Kim, Yann Dauphin, Gintare Karolina Dziugaite, Pablo Samuel Castro, Utku Evci, Conference on Parsimony and Learning, PMLR 234:515-528, 2024. https://proceedings.mlr.press/v234/lee24a.html https://proceedings.mlr.press/v234/lee24a/lee24a.pdf https://openreview.net/forum?id=H2rCZCfXkS https://openreview.net/pdf?id=H2rCZCfXkS
• Eric Samikwa, 2024, Resource-Aware Distributed Machine Learning for Artificial Intelligence of Things, Ph.D. thesis, Faculty of Science, University of Bern, Switzerland, https://boristheses.unibe.ch/5378/1/24samikwa_e_1_.pdf https://doi.org/10.48549/5378 (Multi-edge device with early exit, "micro-split" scheduling, split/federated learning, and distributed inference.)
• Yue Zheng, Yuhao Chen, Bin Qian, Xiufang Shi, Yuanchao Shu, Jiming Chen, 29 Sep 2024, A Review on Edge Large Language Models: Design, Execution, and Applications, https://arxiv.org/abs/2410.11845
• Shengwen Ding, Chenhui Hu, 24 Nov 2024, eFedLLM: Efficient LLM Inference Based on Federated Learning, https://arxiv.org/abs/2411.16003
• Natalie Lang, Alejandro Cohen, Nir Shlezinger, 27 Mar 2024, Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates, https://arxiv.org/abs/2403.18375
• Chengxi Li, Ming Xiao, Mikael Skoglund, 22 Mar 2024, Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation, https://arxiv.org/abs/2403.14905
• Andrew Hard, Antonious M. Girgis, Ehsan Amid, Sean Augenstein, Lara McConnaughey, Rajiv Mathews, Rohan Anil, 14 Mar 2024, Learning from straggler clients in federated learning, https://arxiv.org/abs/2403.09086
• Hongpeng Guo, Haotian Gu, Xiaoyang Wang, Bo Chen, Eun Kyung Lee, Tamar Eilam, Deming Chen, Klara Nahrstedt, 31 Jan 2024, FedCore: Straggler-Free Federated Learning with Distributed Coresets, https://arxiv.org/abs/2402.00219
• Frederico Vicente, Cláudia Soares, Dušan Jakovetić, 13 May 2025, Modular Federated Learning: A Meta-Framework Perspective, https://arxiv.org/abs/2505.08646
• Keke Gai, Dongjue Wang, Jing Yu, Liehuang Zhu, Qi Wu, 14 Aug 2025, A Vision-Language Pre-training Model-Guided Approach for Mitigating Backdoor Attacks in Federated Learning, https://arxiv.org/abs/2508.10315
• Kejia Fan, Jianheng Tang, Zhirui Yang, Feijiang Han, Jiaxu Li, Run He, Yajiang Huang, Anfeng Liu, Houbing Herbert Song, Yunhuai Liu, Huiping Zhuang, 14 Aug 2025, APFL: Analytic Personalized Federated Learning via Dual-Stream Least Squares, https://arxiv.org/abs/2508.10732
• Rodrigo Tertulino, 6 Aug 2025, A Robust Pipeline for Differentially Private Federated Learning on Imbalanced Clinical Data using SMOTETomek and FedProx, https://arxiv.org/abs/2508.10017
• Jane Carney, Kushal Upreti, Gaby G. Dagher, Tim Andersen, 11 Aug 2025, FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning, https://arxiv.org/abs/2508.10042
• Tianjun Yuan, Jiaxiang Geng, Pengchao Han, Xianhao Chen, Bing Luo, 14 Aug 2025, Flexible Personalized Split Federated Learning for On-Device Fine-Tuning of Foundation Models, https://arxiv.org/abs/2508.10349
• Wenxuan Ye, Xueli An, Junfan Wang, Xueqiang Yan, Georg Carle, 14 Aug 2025, FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View, https://arxiv.org/abs/2507.20871
• Murtaza Rangwala, KR Venugopal, Rajkumar Buyya, 14 Aug 2025, Blockchain-Enabled Federated Learning, https://arxiv.org/abs/2508.06406
• Mattia Sabella and Monica Vitali, 23 Jul 2025, Eco-Friendly AI: Unleashing Data Power for Green Federated Learning, https://arxiv.org/abs/2507.17241
• Aritz P\'erez, Carlos Echegoyen and Guzm\'an Santaf\'e, 23 Jul 2025, Decentralized Federated Learning of Probabilistic Generative Classifiers, https://arxiv.org/abs/2507.17285
• Amandeep Singh Bhatia, Sabre Kais, 23 Jul 2025, Enhancing Quantum Federated Learning with Fisher Information-Based Optimization, https://arxiv.org/abs/2507.17580
• Dario Fenoglio, Gabriele Dominici, Pietro Barbiero, Alberto Tonda, Martin Gjoreski, Marc Langheinrich, 23 Jul 2025, Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning, https://arxiv.org/abs/2405.15632
• Mehdi Khalaj, Shahrzad Golestani Najafabadi, Julita Vassileva, 23 Jul 2025, Privacy-Preserving Multimodal News Recommendation through Federated Learning, https://arxiv.org/abs/2507.15460
• Binbin Ding, Penghui Yang, Sheng-Jun Huang, 22 Jul 2025, FLAIN: Mitigating Backdoor Attacks in Federated Learning via Flipping Weight Updates of Low-Activation Input Neurons, https://arxiv.org/abs/2408.08655
• Seung-Wook Kim, Seongyeol Kim, Jiah Kim, Seowon Ji, Se-Ho Lee, 22 Jul 2025, FedWSQ: Efficient Federated Learning with Weight Standardization and Distribution-Aware Non-Uniform Quantization, https://arxiv.org/abs/2506.23516
• Baran Can G\"ul, Suraksha Nadig, Stefanos Tziampazis, Nasser Jazdi, Michael Weyrich, 22 Jul 2025, FedMultiEmo: Real-Time Emotion Recognition via Multimodal Federated Learning, https://arxiv.org/abs/2507.15470
• Obaidullah Zaland, Chanh Nguyen, Florian T. Pokorny and Monowar Bhuyan, 23 Jul 2025, Federated Learning for Large-Scale Cloud Robotic Manipulation: Opportunities and Challenges, https://arxiv.org/abs/2507.17903
• Ahmad Alhonainy (1), Praveen Rao (1) ((1) University of Missouri, USA), 19 Jul 2025, Caching Techniques for Reducing the Communication Cost of Federated Learning in IoT Environments, https://arxiv.org/abs/2507.17772
• Constantin Philippenko and Aymeric Dieuleveut, 24 Jul 2025, Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning, https://arxiv.org/abs/2308.01358
• Daniel Commey, Kamel Abbad, Garth V. Crosby and Lyes Khoukhi, 18 Jul 2025, FedSkipTwin: Digital-Twin-Guided Client Skipping for Communication-Efficient Federated Learning, https://arxiv.org/abs/2507.13624
• Sahar Ghoflsaz Ghinani and Elaheh Sadredini, 18 Jul 2025, FuSeFL: Fully Secure and Scalable Cross-Silo Federated Learning, https://arxiv.org/abs/2507.13591
• Di Yu, Xin Du, Linshan Jiang, Huijing Zhang, Shuiguang Deng, 18 Jul 2025, Exploiting Label Skewness for Spiking Neural Networks in Federated Learning, https://arxiv.org/abs/2412.17305
• Huan Wang, Haoran Li, Huaming Chen, Jun Yan, Jiahua Shi, Jun Shen, 18 Jul 2025, FedDifRC: Unlocking the Potential of Text-to-Image Diffusion Models in Heterogeneous Federated Learning, https://arxiv.org/abs/2507.06482
• Zhiyong Jin, Runhua Xu, Chao Li, Yizhong Liu, Jianxin Li, 18 Jul 2025, Sparsification Under Siege: Defending Against Poisoning Attacks in Communication-Efficient Federated Learning, https://arxiv.org/abs/2505.01454
• Nuria Rodr\'iguez-Barroso and Mario Garc\'ia-M\'arquez and M. Victoria Luz\'on and Francisco Herrera, 21 Jul 2025, Challenges of Trustworthy Federated Learning: What's Done, Current Trends and Remaining Work, https://arxiv.org/abs/2507.15796
• Yajiao Dai, Jun Li, Zhen Mei, Yiyang Ni, Shi Jin, Zengxiang Li, Sheng Guo, Wei Xiang, 12 Jul 2025, Semi-Supervised Federated Learning via Dual Contrastive Learning and Soft Labeling for Intelligent Fault Diagnosis, https://arxiv.org/abs/2507.14181
• Md Rafid Haque, Abu Raihan Mostofa Kamal, Md. Azam Hossain, 18 Jul 2025, FedStrategist: A Meta-Learning Framework for Adaptive and Robust Aggregation in Federated Learning, https://arxiv.org/abs/2507.14322
• Tianle Li, Yongzhi Huang, Linshan Jiang, Qipeng Xie, Chang Liu, Wenfeng Du, Lu Wang, and Kaishun Wu, 20 Jul 2025, FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios, https://arxiv.org/abs/2507.14980
• Yunfeng Li, Junhong Liu, Zhaohui Yang, Guofu Liao, Chuyun Zhang, 20 Jul 2025, Clustered Federated Learning for Generalizable FDIA Detection in Smart Grids with Heterogeneous Data, https://arxiv.org/abs/2507.14999
• Huiling Yang, Zhanwei Wang, and Kaibin Huang, 21 Jul 2025, Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity, https://arxiv.org/abs/2507.15601
• Juntao Tan, Anran Li, Quanchao Liu, Peng Ran, Lan Zhang, 19 Jul 2025, VTarbel: Targeted Label Attack with Minimal Knowledge on Detector-enhanced Vertical Federated Learning, https://arxiv.org/abs/2507.14625
• Juntao Tan, Lan Zhang, Zhonghao Hu, Kai Yang, Peng Ran, Bo Li, 19 Jul 2025, VMask: Tunable Label Privacy Protection for Vertical Federated Learning via Layer Masking, https://arxiv.org/abs/2507.14629
• Khoa Nguyen, Tanveer Khan, Antonis Michalas, 20 Jul 2025, A Privacy-Centric Approach: Scalable and Secure Federated Learning Enabled by Hybrid Homomorphic Encryption, https://arxiv.org/abs/2507.14853
• Zhipeng Wang, Nanqing Dong, Jiahao Sun, William Knottenbelt, Yike Guo, 21 Jul 2025, zkFL: Zero-Knowledge Proof-based Gradient Aggregation for Federated Learning, https://arxiv.org/abs/2310.02554
• Shunsuke Yoneda, Valdemar \v{S}v\'abensk\'y, Gen Li, Daisuke Deguchi, Atsushi Shimada, 21 Jul 2025, Ranking-Based At-Risk Student Prediction Using Federated Learning and Differential Features, https://arxiv.org/abs/2505.09287
• Xinglin Zhao, Yanwen Wang, Xiaobo Liu, Yanrong Hao, Rui Cao, Xin Wen, 8 Aug 2025, A Federated Learning Framework for Handling Subtype Confounding and Heterogeneity in Large-Scale Neuroimaging Diagnosis, https://arxiv.org/abs/2508.06589
• Md. Akmol Masud, Md Abrar Jahin, Mahmud Hasan, 8 Aug 2025, Stabilizing Federated Learning under Extreme Heterogeneity with HeteRo-Select, https://arxiv.org/abs/2508.06692
• Yashwant Krishna Pagoti, Arunesh Sinha, Shamik Sural, 10 Aug 2025, Strategic Incentivization for Locally Differentially Private Federated Learning, https://arxiv.org/abs/2508.07138
• Chenchen Lin, Xuehe Wang, 11 Aug 2025, Multi-Hop Privacy Propagation for Differentially Private Federated Learning in Social Networks, https://arxiv.org/abs/2508.07676
• Mohamad Assaad, Zeinab Nehme, Merouane Debbah, 11 Aug 2025, Communication-Efficient Zero-Order and First-Order Federated Learning Methods over Wireless Networks, https://arxiv.org/abs/2508.08013
• Maozhen Zhang, Mengnan Zhao, Bo Wang, 11 Aug 2025, BadPromptFL: A Novel Backdoor Threat to Prompt-based Federated Learning in Multimodal Models, https://arxiv.org/abs/2508.08040
• Cem Ata Baykara, Saurav Raj Pandey, Ali Burak \"Unal, Harlin Lee, and Mete Akg\"un, 11 Aug 2025, Federated Learning for Epileptic Seizure Prediction Across Heterogeneous EEG Datasets, https://arxiv.org/abs/2508.08159
• Roopkatha Banerjee, Sampath Koti, Gyanendra Singh, Anirban Chakraborty, Gurunath Gurrala, Bhushan Jagyasi and Yogesh Simmhan, 11 Aug 2025, Optimizing Federated Learning for Scalable Power-demand Forecasting in Microgrids, https://arxiv.org/abs/2508.08022
• Zilong Zhao, Robert Birke, Aditya Kunar, Lydia Y. Chen, 11 Aug 2025, Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data, https://arxiv.org/abs/2108.07927
• Dawood Wasif, Dian Chen, Sindhuja Madabushi, Nithin Alluru, Terrence J. Moore, Jin-Hee Cho, 9 Aug 2025, Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AI, https://arxiv.org/abs/2503.16233
• Kaveen Hiniduma, Zilinghan Li, Aditya Sinha, Ravi Madduri, Suren Byna, 11 Aug 2025, CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning, https://arxiv.org/abs/2505.23849
• Ali Shakeri, Wei Emma Zhang, Amin Beheshti, Weitong Chen, Jian Yang and Lishan Yang, 22 Jul 2025, FedDPG: An Adaptive Yet Efficient Prompt-tuning Approach in Federated Learning Settings, https://arxiv.org/abs/2507.19534
• Youngjoon Lee, Hyukjoon Lee, Jinu Gong, Yang Cao, Joonhyuk Kang, 26 Jul 2025, Debunking Optimization Myths in Federated Learning for Medical Image Classification, https://arxiv.org/abs/2507.19822
• Liu junkang and Yuanyuan Liu and Fanhua Shang and Hongying Liu and Jin Liu and Wei Feng, 26 Jul 2025, FedSWA: Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging, https://arxiv.org/abs/2507.20016
• Shuaipeng Zhang, Lanju Kong, Yixin Zhang, Wei He, Yongqing Zheng, Han Yu, Lizhen Cui, 28 Jul 2025, DAG-AFL:Directed Acyclic Graph-based Asynchronous Federated Learning, https://arxiv.org/abs/2507.20571
• Wenxuan Bao, Ruxi Deng, Ruizhong Qiu, Tianxin Wei, Hanghang Tong, Jingrui He, 29 Jul 2025, Latte: Collaborative Test-Time Adaptation of Vision-Language Models in Federated Learning, https://arxiv.org/abs/2507.21494
• Sven Lankester, Manel Slokom, Gustavo de Carvalho Bertoli, Matias Vizcaino, Emmanuelle Beauxis Aussalet, Laura Hollink, 15 Jul 2025, FedFlex: Federated Learning for Diverse Netflix Recommendations, https://arxiv.org/abs/2507.21115
• Xinhai Yan, Libing Wu, Zhuangzhuang Zhang, Bingyi Liu, Lijuan Huo, Jing Wang, 26 Jul 2025, FedBAP: Backdoor Defense via Benign Adversarial Perturbation in Federated Learning, https://arxiv.org/abs/2507.21177
• Abdelrhman Gaber, Hassan Abd-Eltawab, John Elgallab, Youssif Abuzied, Dineo Mpanya, Turgay Celik, Swarun Kumar, Tamer ElBatt, 30 Jul 2025, FedCVD++: Communication-Efficient Federated Learning for Cardiovascular Risk Prediction with Parametric and Non-Parametric Model Optimization, https://arxiv.org/abs/2507.22963
• David J Goetze, Dahlia J Felten, Jeannie R Albrecht, Rohit Bhattacharya, 30 Jul 2025, FLOSS: Federated Learning with Opt-Out and Straggler Support, https://arxiv.org/abs/2507.23115
• Mohammad Karami, Fatemeh Ghassemi, Hamed Kebriaei, Hamid Azadegan, 31 Jul 2025, OptiGradTrust: Byzantine-Robust Federated Learning with Multi-Feature Gradient Analysis and Reinforcement Learning-Based Trust Weighting, https://arxiv.org/abs/2507.23638
• Taeheon Lim, Joohyung Lee, Kyungjae Lee, Jungchan Cho, 31 Jul 2025, Mitigating Resolution-Drift in Federated Learning: Case of Keypoint Detection, https://arxiv.org/abs/2507.23461
• Chen Zhang, Husheng Li, Xiang Liu, Linshan Jiang, Danxin Wang, 30 Jul 2025, Hypernetworks for Model-Heterogeneous Personalized Federated Learning, https://arxiv.org/abs/2507.22330
• Wei Guo, Yiyang Duan, Zhaojun Hu, Yiqi Tong, Fuzhen Zhuang, Xiao Zhang, Jin Dong, Ruofan Wu, Tengfei Liu, Yifan Sun, 30 Jul 2025, Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data, https://arxiv.org/abs/2507.22488
• Zhuocheng Liu, Zhishu Shen, Qiushi Zheng, Tiehua Zhang, Zheng Lei, Jiong Jin, 30 Jul 2025, A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks, https://arxiv.org/abs/2507.22339
• Hongye Wang, Zhaoye Pan, Chang He, Jiaxiang Li, Bo Jiang, 30 Jul 2025, Federated Learning on Riemannian Manifolds: A Gradient-Free Projection-Based Approach, https://arxiv.org/abs/2507.22855
• Bokun Wang and Axel Berg and Durmus Alp Emre Acar and Chuteng Zhou, 30 Jul 2025, Towards Federated Learning with On-device Training and Communication in 8-bit Floating Point, https://arxiv.org/abs/2407.02610
• Minyeong Choe, Cheolhee Park, Changho Seo, and Hyunil Kim, 30 Jul 2025, SDBA: A Stealthy and Long-Lasting Durable Backdoor Attack in Federated Learning, https://arxiv.org/abs/2409.14805
• Hanchi Ren and Jingjing Deng and Xianghua Xie, 1 Aug 2025, Gradient Leakage Defense with Key-Lock Module for Federated Learning, https://arxiv.org/abs/2305.04095
• Honoka Anada, Tatsuya Kaneko, Shinya Takamaeda-Yamazaki, 1 Aug 2025, How to Evaluate Participant Contributions in Decentralized Federated Learning, https://arxiv.org/abs/2505.23246
• Hangyu Li and Hongyue Wu and Guodong Fan and Zhen Zhang and Shizhan Chen and Zhiyong Feng, 1 Aug 2025, Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices, https://arxiv.org/abs/2506.20644
• Jinnan Guo, Kapil Vaswani, Andrew Paverd, Peter Pietzuch, 1 Aug 2025, ExclaveFL: Providing Transparency to Federated Learning using Exclaves, https://arxiv.org/abs/2412.10537
• Xin Chen, Shuaijun Chen, Omid Tavallaie, Nguyen Tran, Shuhuang Xiang, Albert Zomaya, 2 Aug 2025, Convergence Analysis of Aggregation-Broadcast in LoRA-enabled Federated Learning, https://arxiv.org/abs/2508.01348
• Heting Liu, Junzhe Huang, Fang He, Guohong Cao, 3 Aug 2025, Dynamic Clustering for Personalized Federated Learning on Heterogeneous Edge Devices, https://arxiv.org/abs/2508.01580
• Ziru Niu, Hai Dong, A.K. Qin, 3 Aug 2025, Boosting Generalization Performance in Model-Heterogeneous Federated Learning Using Variational Transposed Convolution, https://arxiv.org/abs/2508.01669
• Ali Forootani, Raffaele Iervolino, 3 Aug 2025, Asynchronous Federated Learning with non-convex client objective functions and heterogeneous dataset, https://arxiv.org/abs/2508.01675
• Xiangwang Hou, Jingjing Wang, Fangming Guan, Jun Du, Chunxiao Jiang, Yong Ren, 3 Aug 2025, Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design, https://arxiv.org/abs/2508.01745
• Ignacy St\k{e}pka, Nicholas Gisolfi, Kacper Tr\k{e}bacz, Artur Dubrawski, 3 Aug 2025, Mitigating Persistent Client Dropout in Asynchronous Decentralized Federated Learning, https://arxiv.org/abs/2508.01807
• Qi Xiong, Hai Dong, Nasrin Sohrabi, Zahir Tari, 4 Aug 2025, FedLAD: A Linear Algebra Based Data Poisoning Defence for Federated Learning, https://arxiv.org/abs/2508.02136
• Mirko Konstantin, Moritz Fuchs and Anirban Mukhopadhyay, 4 Aug 2025, ASMR: Angular Support for Malfunctioning Client Resilience in Federated Learning, https://arxiv.org/abs/2508.02414
• Shunxian Gu, Chaoqun You, Bangbang Ren, Deke Guo, 4 Aug 2025, Communication and Computation Efficient Split Federated Learning in O-RAN, https://arxiv.org/abs/2508.02534
• Junjie Shan, Ziqi Zhao, Jialin Lu, Rui Zhang, Siu Ming Yiu and Ka-Ho Chow, 2 Aug 2025, Geminio: Language-Guided Gradient Inversion Attacks in Federated Learning, https://arxiv.org/abs/2411.14937
• Sota Mashiko, Yuji Kawamata, Tomoru Nakayama, Tetsuya Sakurai, Yukihiko Okada, 1 Aug 2025, Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach, https://arxiv.org/abs/2501.12723
• Keke Gai, Mohan Wang, Jing Yu, Dongjue Wang, Qi Wu, 3 Aug 2025, Adaptive Prototype Knowledge Transfer for Federated Learning with Mixed Modalities and Heterogeneous Tasks, https://arxiv.org/abs/2502.04400
• Jiahui Bai, Hai Dong, A. K. Qin, 5 Aug 2025, On the Fast Adaptation of Delayed Clients in Decentralized Federated Learning: A Centroid-Aligned Distillation Approach, https://arxiv.org/abs/2508.02993
• Weiyao Zhang, Jinyang Li, Qi Song, Miao Wang, Chungang Lin, Haitong Luo, Xuying Meng, Yujun Zhang, 5 Aug 2025, Heterogeneity-Oblivious Robust Federated Learning, https://arxiv.org/abs/2508.03579
• Hao Di, Yi Yang, Haishan Ye, Xiangyu Chang, 5 Aug 2025, PPFL: A Personalized Federated Learning Framework for Heterogeneous Population, https://arxiv.org/abs/2310.14337
• Hyungbin Kim, Incheol Baek, Yon Dohn Chung, 6 Aug 2025, Decoupled Contrastive Learning for Federated Learning, https://arxiv.org/abs/2508.04005
• Tuan Nguyen, Khoa D Doan, and Kok-Seng Wong, 6 Aug 2025, FLAT: Latent-Driven Arbitrary-Target Backdoor Attacks in Federated Learning, https://arxiv.org/abs/2508.04064
• Jianheng Tang, Zhirui Yang, Jingchao Wang, Kejia Fan, Jinfeng Xu, Huiping Zhuang, Anfeng Liu, Houbing Herbert Song, Leye Wang, Yunhuai Liu, 6 Aug 2025, FedHiP: Heterogeneity-Invariant Personalized Federated Learning Through Closed-Form Solutions, https://arxiv.org/abs/2508.04470
• Borui Li, Li Yan, Junhao Han, Jianmin Liu, Lei Yu, 6 Aug 2025, SenseCrypt: Sensitivity-guided Selective Homomorphic Encryption for Joint Federated Learning in Cross-Device Scenarios, https://arxiv.org/abs/2508.04100
• Borui Li, Li Yan, Jianmin Liu, 6 Aug 2025, SelectiveShield: Lightweight Hybrid Defense Against Gradient Leakage in Federated Learning, https://arxiv.org/abs/2508.04265
• Jiahao Xu, Rui Hu, Olivera Kotevska, Zikai Zhang, 5 Aug 2025, Traceable Black-box Watermarks for Federated Learning, https://arxiv.org/abs/2505.13651
• Thinh Nguyen, Le Huy Khiem, Van-Tuan Tran, Khoa D Doan, Nitesh V Chawla, Kok-Seng Wong, 7 Aug 2025, pFedDSH: Enabling Knowledge Transfer in Personalized Federated Learning through Data-free Sub-Hypernetwork, https://arxiv.org/abs/2508.05157
• Mirko Konstantin and Anirban Mukhopadhyay, 7 Aug 2025, Don't Reach for the Stars: Rethinking Topology for Resilient Federated Learning, https://arxiv.org/abs/2508.05224
• Qinghua Yao, Xiangrui Xu, Zhize Li, 7 Aug 2025, X-VFL: A New Vertical Federated Learning Framework with Cross Completion and Decision Subspace Alignment, https://arxiv.org/abs/2508.05568
• Sachin Dudda Nagaraju, Ashkan Moradi, Bendik Skarre Abrahamsen, and Mattijs Elschot, 7 Aug 2025, FedGIN: Federated Learning with Dynamic Global Intensity Non-linear Augmentation for Organ Segmentation using Multi-modal Images, https://arxiv.org/abs/2508.05137
• Ce Na, Kai Yang, Dengzhao Fang, Yu Li, Jingtong Gao, Chengcheng Zhu, Jiale Zhang, Xiaobing Sun, Yi Chang, 8 Aug 2025, Graph Federated Learning for Personalized Privacy Recommendation, https://arxiv.org/abs/2508.06208
• Yuze Liu, Tiehua Zhang, Zhishu Shen, Libing Wu, Shiping Chen and Jiong Jin, 1 Aug 2025, Towards Heterogeneity-Aware and Energy-Efficient Topology Optimization for Decentralized Federated Learning in Edge Environment, https://arxiv.org/abs/2508.08278
• Dung T. Tran, Nguyen B. Ha, Van-Dinh Nguyen, Kok-Seng Wong, 11 Aug 2025, SHeRL-FL: When Representation Learning Meets Split Learning in Hierarchical Federated Learning, https://arxiv.org/abs/2508.08339
• Keumseo Ryum, Jinu Gong, and Joonhyuk Kang, 12 Aug 2025, SHEFL: Resource-Aware Aggregation and Sparsification in Heterogeneous Ensemble Federated Learning, https://arxiv.org/abs/2508.08552
• Wenyou Guo, Ting Qu, Chunrong Pan, George Q. Huang, 12 Aug 2025, Distributed optimization: designed for federated learning, https://arxiv.org/abs/2508.08606
• Yuvraj Dutta, Soumyajit Chatterjee, Sandip Chakraborty, Basabdatta Palit, 11 Aug 2025, Benchmarking Federated Learning for Throughput Prediction in 5G Live Streaming Applications, https://arxiv.org/abs/2508.08479
• Davide Domini, Gianluca Aguzzi, Lukas Esterle and Mirko Viroli, 12 Aug 2025, FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated Learning, https://arxiv.org/abs/2502.08577
• Ratun Rahman, 12 Aug 2025, Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence, https://arxiv.org/abs/2504.17703
• Zhekai Zhou, Shudong Liu, Zhaokun Zhou, Yang Liu, Qiang Yang, Yuesheng Zhu, Guibo Luo, 7 Aug 2025, FedMP: Tackling Medical Feature Heterogeneity in Federated Learning from a Manifold Perspective, https://arxiv.org/abs/2508.09174
• Jinghong Tan, Zhian Liu, Kun Guo, Mingxiong Zhao, 7 Aug 2025, Long-Term Client Selection for Federated Learning with Non-IID Data: A Truthful Auction Approach, https://arxiv.org/abs/2508.09181
• Zikai Zhang, Suman Rath, Jiahao Xu, Tingsong Xiao, 13 Aug 2025, Federated Learning for Smart Grid: A Survey on Applications and Potential Vulnerabilities, https://arxiv.org/abs/2409.10764
• Heqiang Wang, Weihong Yang, Xiaoxiong Zhong, Jia Zhou, Fangming Liu, Weizhe Zhang, 15 Aug 2025, Mitigating Modality Quantity and Quality Imbalance in Multimodal Online Federated Learning, https://arxiv.org/abs/2508.11159
• Martin Pelikan, Sheikh Shams Azam, Vitaly Feldman, Jan "Honza" Silovsky, Kunal Talwar, Christopher G. Brinton, Tatiana Likhomanenko, 14 Aug 2025, Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping, https://arxiv.org/abs/2310.00098
• You Hak Lee, Xiaofan Yu, Quanling Zhao, Flavio Ponzina, Tajana Rosing, 16 Aug 2025, FedUHD: Unsupervised Federated Learning using Hyperdimensional Computing, https://arxiv.org/abs/2508.12021
• Zahra Kharaghani, Ali Dadras, Tommy L\"ofstedt, 16 Aug 2025, Fairness Regularization in Federated Learning, https://arxiv.org/abs/2508.12042
• Emmanouil Kritharakis, Dusan Jakovetic, Antonios Makris, Konstantinos Tserpes, 18 Aug 2025, Robust Federated Learning under Adversarial Attacks via Loss-Based Client Clustering, https://arxiv.org/abs/2508.12672
• Yuhao Zhou, Jindi Lv, Yuxin Tian, Dan Si, Qing Ye, Jiancheng Lv, 18 Aug 2025, Deploying Models to Non-participating Clients in Federated Learning without Fine-tuning: A Hypernetwork-based Approach, https://arxiv.org/abs/2508.12673
• Beomseok Seo, Kichang Lee, JaeYeon Park, 18 Aug 2025, FedUNet: A Lightweight Additive U-Net Module for Federated Learning with Heterogeneous Models, https://arxiv.org/abs/2508.12740
• Yue Xia, Tayyebeh Jahani-Nezhad and Rawad Bitar, 18 Aug 2025, Fed-DPRoC:Communication-Efficient Differentially Private and Robust Federated Learning, https://arxiv.org/abs/2508.12978
• Xiaojin Zhang, Mingcong Xu, Yiming Li, Wei Chen, Qiang Yang, 16 Aug 2025, Deciphering the Interplay between Attack and Protection Complexity in Privacy-Preserving Federated Learning, https://arxiv.org/abs/2508.11907
• Ratun Rahman, Atit Pokharel, Md Raihan Uddin, and Dinh C. Nguyen, 17 Aug 2025, SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualization, https://arxiv.org/abs/2508.12477
• Jihyun Lim, Junhyuk Jo, Tuo Zhang, Sunwoo Lee, 17 Aug 2025, Enabling Weak Client Participation via On-device Knowledge Distillation in Heterogenous Federated Learning, https://arxiv.org/abs/2503.11151
• Shiwei Li, Xiandi Luo, Haozhao Wang, Xing Tang, Shijie Xu, Weihong Luo, Yuhua Li, Xiuqiang He, Ruixuan Li, 17 Aug 2025, The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning, https://arxiv.org/abs/2505.23176
• SeungBum Ha, Taehwan Lee, Jiyoun Lim, Sung Whan Yoon, 17 Aug 2025, Benchmarking Federated Learning for Semantic Datasets: Federated Scene Graph Generation, https://arxiv.org/abs/2412.10436
• Wenxuan Ye, Xueli An, Onur Ayan, Junfan Wang, Xueqiang Yan, Georg Carle, 19 Aug 2025, Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models, https://arxiv.org/abs/2508.13625
• Wenfei Liang, Yanan Zhao, Rui She, Yiming Li and Wee Peng Tay, 19 Aug 2025, Personalized Subgraph Federated Learning with Sheaf Collaboration, https://arxiv.org/abs/2508.13642
• Jie Shi, Arno P. J. M. Siebes, Siamak Mehrkanoon, 19 Aug 2025, Trans-XFed: An Explainable Federated Learning for Supply Chain Credit Assessment, https://arxiv.org/abs/2508.13715
• Sergey Skorik, Vladislav Dorofeev, Gleb Molodtsov, Aram Avetisyan, Dmitry Bylinkin, Daniil Medyakov, Aleksandr Beznosikov, 19 Aug 2025, Communication-Efficient Federated Learning with Adaptive Number of Participants, https://arxiv.org/abs/2508.13803
• Daniel M. Jimenez-Gutierrez, Yelizaveta Falkouskaya, Jose L. Hernandez-Ramos, Aris Anagnostopoulos, Ioannis Chatzigiannakis, Andrea Vitaletti, 19 Aug 2025, On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions, https://arxiv.org/abs/2508.13730
• Charlie Hou, Mei-Yu Wang, Yige Zhu, Daniel Lazar, Giulia Fanti, 19 Aug 2025, POPri: Private Federated Learning using Preference-Optimized Synthetic Data, https://arxiv.org/abs/2504.16438
• Nazatul Haque Sultan, Yan Bo, Yansong Gao, Seyit Camtepe, Arash Mahboubi, Hang Thanh Bui, Aufeef Chauhan, Hamed Aboutorab, Michael Bewong, Dineshkumar Singh, Praveen Gauravaram, Rafiqul Islam, and Sharif Abuadbba, 19 Aug 2025, Setup Once, Secure Always: A Single-Setup Secure Federated Learning Aggregation Protocol with Forward and Backward Secrecy for Dynamic Users, https://arxiv.org/abs/2502.08989
• Tao Shen, Zexi Li, Didi Zhu, Ziyu Zhao, Chao Wu, Fei Wu, 20 Aug 2025, FedEve: On Bridging the Client Drift and Period Drift for Cross-device Federated Learning, https://arxiv.org/abs/2508.14539
• Yichen Li, Xiuying Wang, Wenchao Xu, Haozhao Wang, Yining Qi, Jiahua Dong, Ruixuan Li, 20 Aug 2025, Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning, https://arxiv.org/abs/2507.10348
• Miha O\v{z}bot, Igor \v{S}krjanc, 21 Aug 2025, Federated Learning based on Self-Evolving Gaussian Clustering, https://arxiv.org/abs/2508.15393
• Bingguang Lu, Hongsheng Hu, Yuantian Miao, Shaleeza Sohail, Chaoxiang He, Shuo Wang, Xiao Chen, 21 Aug 2025, BadFU: Backdoor Federated Learning through Adversarial Machine Unlearning, https://arxiv.org/abs/2508.15541
• Lishan Yang, Wei Emma Zhang, Quan Z. Sheng, Lina Yao, Weitong Chen and Ali Shakeri, 21 Aug 2025, MMiC: Mitigating Modality Incompleteness in Clustered Federated Learning, https://arxiv.org/abs/2505.06911
• Dinh C. Nguyen, Md Raihan Uddin, Shaba Shaon, Ratun Rahman, Octavia Dobre, and Dusit Niyato, 21 Aug 2025, Quantum Federated Learning: A Comprehensive Survey, https://arxiv.org/abs/2508.15998
• Renxuan Tan, Rongpeng Li, Xiaoxue Yu, Xianfu Chen, Xing Xu, and Zhifeng Zhao, 22 Aug 2025, Pareto Actor-Critic for Communication and Computation Co-Optimization in Non-Cooperative Federated Learning Services, https://arxiv.org/abs/2508.16037
• Guangyu Sun, Jingtao Li, Weiming Zhuang, Chen Chen, Chen Chen, Lingjuan Lyu, 22 Aug 2025, Closer to Reality: Practical Semi-Supervised Federated Learning for Foundation Model Adaptation, https://arxiv.org/abs/2508.16568
• Bibo Wu, Fang Fang, Ming Zeng and Xianbin Wang, 17 Aug 2025, Straggler-Resilient Federated Learning over A Hybrid Conventional and Pinching Antenna Network, https://arxiv.org/abs/2508.15821
• Zhenan Fan, Huang Fang, Xinglu Wang, Zirui Zhou, Jian Pei, Michael P. Friedlander, Yong Zhang, 21 Aug 2025, Fair and efficient contribution valuation for vertical federated learning, https://arxiv.org/abs/2201.02658
• Xinyu Zhou, Jun Zhao, Huimei Han, Claude Guet, 22 Aug 2025, Joint Optimization of Energy Consumption and Completion Time in Federated Learning, https://arxiv.org/abs/2209.14900
• Seunghun Yu, Jin-Hyun Ahn, Joonhyuk Kang, 22 Aug 2025, FedEFC: Federated Learning Using Enhanced Forward Correction Against Noisy Labels, https://arxiv.org/abs/2504.05615
• Tao Liu, Xuehe Wang, 23 Aug 2025, Degree of Staleness-Aware Data Updating in Federated Learning, https://arxiv.org/abs/2508.16931
• Jiaqi Zhu, Bikramjit Das, Yong Xie, Nikolaos Pappas, and Howard H. Yang, 25 Aug 2025, Rethinking Federated Learning Over the Air: The Blessing of Scaling Up, https://arxiv.org/abs/2508.17697
• Ming Yang, Dongrun Li, Xin Wang, Xiaoyang Yu, Xiaoming Wu, Shibo He, 25 Aug 2025, Choice Outweighs Effort: Facilitating Complementary Knowledge Fusion in Federated Learning via Re-calibration and Merit-discrimination, https://arxiv.org/abs/2508.17954
• Emmanouil Kritharakis and Antonios Makris and Dusan Jakovetic and Konstantinos Tserpes, 25 Aug 2025, FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning, https://arxiv.org/abs/2508.18060
• Po-Hsien Yu, Yu-Syuan Tseng, and Shao-Yi Chien, 24 Aug 2025, FedKLPR: Personalized Federated Learning for Person Re-Identification with Adaptive Pruning, https://arxiv.org/abs/2508.17431
• Bishwamittra Ghosh, Debabrota Basu, Fu Huazhu, Wang Yuan, Renuga Kanagavelu, Jiang Jin Peng, Liu Yong, Goh Siow Mong Rick, and Wei Qingsong, 23 Aug 2025, History-Aware and Dynamic Client Contribution in Federated Learning, https://arxiv.org/abs/2403.07151
• Ruofan Jia, Weiying Xie, Jie Lei, Jitao Ma, Haonan Qin, Leyuan Fang, 25 Aug 2025, HeteroTune: Efficient Federated Learning for Large Heterogeneous Models, https://arxiv.org/abs/2411.16796
• Chao Feng, Yuanzhe Gao, Alberto Huertas Celdran, Gerome Bovet, Burkhard Stiller, 25 Aug 2025, From Models to Network Topologies: A Topology Inference Attack in Decentralized Federated Learning, https://arxiv.org/abs/2501.03119
• Harish Karthikeyan and Antigoni Polychroniadou, 24 Aug 2025, $\mathsf{OPA}$: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated Learning, https://arxiv.org/abs/2410.22303
• Zhengyu Wu, Xunkai Li, Yinlin Zhu, Zekai Chen, Guochen Yan, Yanyu Yan, Hao Zhang, Yuming Ai, Xinmo Jin, Rong-Hua Li, and Guoren Wang, 22 Jul 2025, A Comprehensive Data-centric Overview of Federated Graph Learning, https://arxiv.org/abs/2507.16541
• Minh Ngoc Luu, Minh-Duong Nguyen, Ebrahim Bedeer, Van Duc Nguyen, Dinh Thai Hoang, Diep N. Nguyen, Quoc-Viet Pham, 22 Jul 2025, Energy-Efficient and Real-Time Sensing for Federated Continual Learning via Sample-Driven Control, https://arxiv.org/abs/2310.07497
• Zhongzheng Yuan, Lianshuai Guo, Xunkai Li, Yinlin Zhu, Wenyu Wang, Meixia Qu, 24 Jul 2025, FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcasting, https://arxiv.org/abs/2507.18219
• Xu Zhang, Zhenyuan Yuan, Minghui Zhu, 18 Jul 2025, Byzantine-resilient federated online learning for Gaussian process regression, https://arxiv.org/abs/2507.14021
• Ukjo Hwang, Songnam Hong, 19 Jul 2025, Federated Reinforcement Learning in Heterogeneous Environments, https://arxiv.org/abs/2507.14487
• Yujia Mu, Cong Shen, 21 Jul 2025, Federated Split Learning with Improved Communication and Storage Efficiency, https://arxiv.org/abs/2507.15816
• Zihao Hu (1), Jia Yan (2), Ying-Jun Angela Zhang (1) ((1) The Chinese University of Hong Kong, (2) The Hong Kong University of Science and Technology (Guangzhou)), 6 Aug 2025, Communication-Learning Co-Design for Differentially Private Over-the-Air Federated Distillation, https://arxiv.org/abs/2508.06557
• Jingmao Li, Yuanxing Chen, Shuangge Ma, Kuangnan Fang, 8 Aug 2025, Federated Online Learning for Heterogeneous Multisource Streaming Data, https://arxiv.org/abs/2508.06652
• Abhishek Sawaika, Swetang Krishna, Tushar Tomar, Durga Pritam Suggisetti, Aditi Lal, Tanmaya Shrivastav, Nouhaila Innan, Muhammad Shafique, 15 Jul 2025, A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection, https://arxiv.org/abs/2507.22908
• Danni Peng, Yuan Wang, Kangning Cai, Peiyan Ning, Jiming Xu, Yong Liu, Rick Siow Mong Goh, Qingsong Wei, Huazhu Fu, 14 Aug 2025, Improving Learning of New Diseases through Knowledge-Enhanced Initialization for Federated Adapter Tuning, https://arxiv.org/abs/2508.10299
• Xinrui Li, Qilin Fan, Tianfu Wang, Kaiwen Wei, Ke Yu, Xu Zhang, 14 Aug 2025, GraphFedMIG: Tackling Class Imbalance in Federated Graph Learning via Mutual Information-Guided Generation, https://arxiv.org/abs/2508.10471
• Zekai Chen, Xunkai Li, Yinlin Zhu, Rong-Hua Li, Guoren Wang, 14 Aug 2025, Rethinking Client-oriented Federated Graph Learning, https://arxiv.org/abs/2504.14188
• Chengzhuo Han, 28 Jul 2025, Enhancing QoS in Edge Computing through Federated Layering Techniques: A Pathway to Resilient AI Lifelong Learning Systems, https://arxiv.org/abs/2507.20444
• Yebo Wu, Jingguang Li, Zhijiang Guo and Li Li, 31 Jul 2025, Learning Like Humans: Resource-Efficient Federated Fine-Tuning through Cognitive Developmental Stages, https://arxiv.org/abs/2508.00041
• Hung-Chieh Fang, Hsuan-Tien Lin, Irwin King, Yifei Zhang, 2 Aug 2025, Soft Separation and Distillation: Toward Global Uniformity in Federated Unsupervised Learning, https://arxiv.org/abs/2508.01251
• Cui Miao, Tao Chang, Meihan Wu, Hongbin Xu, Chun Li, Ming Li, Xiaodong Wang, 4 Aug 2025, FedVLA: Federated Vision-Language-Action Learning with Dual Gating Mixture-of-Experts for Robotic Manipulation, https://arxiv.org/abs/2508.02190
• Shuo Wang and Keke Gai and Jing Yu and Liehuang Zhu and Qi Wu, 5 Aug 2025, Vertical Federated Continual Learning via Evolving Prototype Knowledge, https://arxiv.org/abs/2502.09152
• Zihan Tan, Suyuan Huang, Guancheng Wan, Wenke Huang, He Li and Mang Ye, 5 Aug 2025, S2FGL: Spatial Spectral Federated Graph Learning, https://arxiv.org/abs/2507.02409
• Shengchao Chen, Guodong Long, Jing Jiang, 6 Aug 2025, FeDaL: Federated Dataset Learning for Time Series Foundation Models, https://arxiv.org/abs/2508.04045
• Jiansheng Rao, Jiayi Li, Zhizhi Gong, Soummya Kar, Haoxuan Li, 7 Aug 2025, Federated Multi-Objective Learning with Controlled Pareto Frontiers, https://arxiv.org/abs/2508.05424
• Junhyeog Yun, Minui Hong, Gunhee Kim, 8 Aug 2025, FedMeNF: Privacy-Preserving Federated Meta-Learning for Neural Fields, https://arxiv.org/abs/2508.06301
• Fuyao Zhang, Xinyu Yan, Tiantong Wu, Wenjie Li, Tianxiang Chen, Yang Cao, Ran Yan, Longtao Huang, Wei Yang Bryan Lim, Qiang Yang, 12 Aug 2025, Oblivionis: A Lightweight Learning and Unlearning Framework for Federated Large Language Models, https://arxiv.org/abs/2508.08875
• Hao Yu, Xin Yang, Boyang Fan, Xuemei Cao, Hanlin Gu, Lixin Fan, Qiang Yang, 13 Aug 2025, Large-Small Model Collaborative Framework for Federated Continual Learning, https://arxiv.org/abs/2508.09489
• Lianshuai Guo, Zhongzheng Yuan, Xunkai Li, Yinlin Zhu, Meixia Qu, Wenyu Wang, 15 Aug 2025, DFed-SST: Building Semantic- and Structure-aware Topologies for Decentralized Federated Graph Learning, https://arxiv.org/abs/2508.11530
• Marcel Gregoriadis, Jingwei Kang, Johan Pouwelse, 17 Aug 2025, A Large-Scale Web Search Dataset for Federated Online Learning to Rank, https://arxiv.org/abs/2508.12353
• Dingzhu Wen, Sijing Xie, Xiaowen Cao, Yuanhao Cui, Jie Xu, Yuanming Shi, and Shuguang Cui, 21 Aug 2025, Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learning, https://arxiv.org/abs/2508.15185
• Hamta Sedghani, Abednego Wamuhindo Kambale, Federica Filippini, Francesca Palermo, Diana Trojaniello, Danilo Ardagna, 24 Aug 2025, Federated Reinforcement Learning for Runtime Optimization of AI Applications in Smart Eyewears, https://arxiv.org/abs/2508.17262
• Omar Bekdache and Naresh Shanbhag, 24 Aug 2025, FedERL: Federated Efficient and Robust Learning for Common Corruptions, https://arxiv.org/abs/2508.17381
• Payam Abdisarabshali, Fardis Nadimi, Kasra Borazjani, Naji Khosravan, Minghui Liwang, Wei Ni, Dusit Niyato, Michael Langberg, Seyyedali Hosseinalipour, 3 Sep 2025, Hierarchical Federated Foundation Models over Wireless Networks for Multi-Modal Multi-Task Intelligence: Integration of Edge Learning with D2D/P2P-Enabled Fog Learning Architectures, https://arxiv.org/abs/2509.03695
• Allan Salihovic, Payam Abdisarabshali, Michael Langberg, Seyyedali Hosseinalipour, 3 Sep 2025, From Federated Learning to $\mathbb{X}$-Learning: Breaking the Barriers of Decentrality Through Random Walks, https://arxiv.org/abs/2509.03709
• Ozgu Goksu and Nicolas Pugeault, 4 Sep 2025, FedQuad: Federated Stochastic Quadruplet Learning to Mitigate Data Heterogeneity, https://arxiv.org/abs/2509.04107
• Cosmin-Andrei Hatfaludi and Alex Serban, 5 Sep 2025, Foundational Models and Federated Learning: Survey, Taxonomy, Challenges and Practical Insights, https://arxiv.org/abs/2509.05142
• Jiaojiao Zhang, Yuqi Xu, Kun Yuan, 5 Sep 2025, An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data, https://arxiv.org/abs/2509.05213
• Walid El Maouaki, Nouhaila Innan, Alberto Marchisio, Taoufik Said, Muhammad Shafique, and Mohamed Bennai, 5 Sep 2025, RobQFL: Robust Quantum Federated Learning in Adversarial Environment, https://arxiv.org/abs/2509.04914
• Zijian Wang, Wei Tong, Tingxuan Han, Haoyu Chen, Tianling Zhang, Yunlong Mao, Sheng Zhong, 5 Sep 2025, On Evaluating the Poisoning Robustness of Federated Learning under Local Differential Privacy, https://arxiv.org/abs/2509.05265
• Miroslav Popovic, Marko Popovic, Miodrag Djukic, Ilija Basicevic, 5 Sep 2025, Translating Federated Learning Algorithms in Python into CSP Processes Using ChatGPT, https://arxiv.org/abs/2506.07173
• Francesco Diana, Andr\'e Nusser, Chuan Xu, Giovanni Neglia, 5 Sep 2025, Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning, https://arxiv.org/abs/2505.10264
• Johan Erbani, Sonia Ben Mokhtar, Pierre-Edouard Portier, Elod Egyed-Zsigmond, Diana Nurbakova, 5 Sep 2025, A Weighted Loss Approach to Robust Federated Learning under Data Heterogeneity, https://arxiv.org/abs/2506.09824
• Jiahao Xu, Rui Hu, Olivera Kotevska, 5 Sep 2025, Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy, https://arxiv.org/abs/2505.13655
• Rodrigo Tertulino, 23 Aug 2025, Evaluating Federated Learning for At-Risk Student Prediction: A Comparative Analysis of Model Complexity and Data Balancing, https://arxiv.org/abs/2508.18316
• Yang Li, Hanjie Wang, Yuanzheng Li, Jiazheng Li, Zhaoyang Dong, 24 Aug 2025, ZTFed-MAS2S: A Zero-Trust Federated Learning Framework with Verifiable Privacy and Trust-Aware Aggregation for Wind Power Data Imputation, https://arxiv.org/abs/2508.18318
• Enrique M\'armol Campos and Aurora Gonz\'alez Vidal and Jos\'e Luis Hern\'andez Ramos and Antonio Skarmeta, 26 Aug 2025, FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks, https://arxiv.org/abs/2508.18737
• Adam Breitholtz and Edvin Listo Zec and Fredrik D. Johansson, 26 Aug 2025, Federated Learning with Heterogeneous and Private Label Sets, https://arxiv.org/abs/2508.18774
• Zhibo Xu, Jianhao Zhu, Jingwen Xu, Changze Lv, Zisu Huang, Xiaohua Wang, Muling Wu, Qi Qian, Xiaoqing Zheng, Xuanjing Huang, 26 Aug 2025, Enhancing Model Privacy in Federated Learning with Random Masking and Quantization, https://arxiv.org/abs/2508.18911
• Md Anwar Hossen, Fatema Siddika, Wensheng Zhang, Anuj Sharma, and Ali Jannesari, 26 Aug 2025, FedProtoKD: Dual Knowledge Distillation with Adaptive Class-wise Prototype Margin for Heterogeneous Federated Learning, https://arxiv.org/abs/2508.19009
• Edvin Listo Zec and Adam Breitholtz and Fredrik D. Johansson, 26 Aug 2025, Overcoming label shift with target-aware federated learning, https://arxiv.org/abs/2411.03799
• Tiandi Ye, Wenyan Liu, Kai Yao, Lichun Li, Shangchao Su, Cen Chen, Xiang Li, Shan Yin, Ming Gao, 27 Aug 2025, Towards Instance-wise Personalized Federated Learning via Semi-Implicit Bayesian Prompt Tuning, https://arxiv.org/abs/2508.19621
• Viktor Valadi, Mattias {\AA}kesson, Johan \"Ostman, Salman Toor, Andreas Hellander, 27 Aug 2025, From Research to Reality: Feasibility of Gradient Inversion Attacks in Federated Learning, https://arxiv.org/abs/2508.19819
• Ferdous Pervej and Minseok Choi and Andreas F. Molisch, 27 Aug 2025, Online-Score-Aided Federated Learning: Taming the Resource Constraints in Wireless Networks, https://arxiv.org/abs/2408.05886
• Atit Pokharel, Ratun Rahman, Shaba Shaon, Thomas Morris and Dinh C. Nguyen, 27 Aug 2025, Differentially Private Federated Quantum Learning via Quantum Noise, https://arxiv.org/abs/2508.20310
• Xiangyu Chang, Sk Miraj Ahmed, Srikanth V. Krishnamurthy, Basak Guler, Ananthram Swami, Samet Oymak, Amit K. Roy-Chowdhury, 28 Aug 2025, FLASH: Federated Learning Across Simultaneous Heterogeneities, https://arxiv.org/abs/2402.08769
• Hossein KhademSohi, Hadi Hemmati, Jiayu Zhou, Steve Drew, 28 Aug 2025, Owen Sampling Accelerates Contribution Estimation in Federated Learning, https://arxiv.org/abs/2508.21261
• Masahiro Hayashitani, Junki Mori, and Isamu Teranishi, 29 Aug 2025, Survey of Privacy Threats and Countermeasures in Federated Learning, https://arxiv.org/abs/2402.00342
• Rodrigo Tertulino, 27 Aug 2025, Centralized vs. Federated Learning for Educational Data Mining: A Comparative Study on Student Performance Prediction with SAEB Microdata, https://arxiv.org/abs/2509.00086
• Minku Kang, Hogun Park, 30 Aug 2025, Curriculum Guided Personalized Subgraph Federated Learning, https://arxiv.org/abs/2509.00402
• Xiangyu Zhang and Mang Ye, 30 Aug 2025, FedThief: Harming Others to Benefit Oneself in Self-Centered Federated Learning, https://arxiv.org/abs/2509.00540
• Noorain Mukhtiar, Adnan Mahmood and Quan Z. Sheng, 31 Aug 2025, Fairness in Federated Learning: Trends, Challenges, and Opportunities, https://arxiv.org/abs/2509.00799
• Olusola Odeyomi, Sofiat Olaosebikan, Ajibuwa Opeyemi, and Oluwadoyinsola Ige, 31 Aug 2025, Online Decentralized Federated Multi-task Learning With Trustworthiness in Cyber-Physical Systems, https://arxiv.org/abs/2509.00992
• Maciej Krzysztof Zuziak, Roberto Pellungrini and Salvatore Rinzivillo, 1 Sep 2025, One-Shot Clustering for Federated Learning Under Clustering-Agnostic Assumption, https://arxiv.org/abs/2509.01587
• Dongseok Kim, Wonjun Jeong, Gisung Oh, 2 Sep 2025, Gaming and Cooperation in Federated Learning: What Can Happen and How to Monitor It, https://arxiv.org/abs/2509.02391
• Rui Zhang, Wenlong Mou, 2 Sep 2025, Federated learning over physical channels: adaptive algorithms with near-optimal guarantees, https://arxiv.org/abs/2509.02538
• Chaoyu Zhang and Heng Jin and Shanghao Shi and Hexuan Yu and Sydney Johns and Y. Thomas Hou and Wenjing Lou, 30 Aug 2025, Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats, https://arxiv.org/abs/2509.00634
• Kai Zhang, Yutong Dai, Hongyi Wang, Eric Xing, Xun Chen, Lichao Sun, 2 Sep 2025, Memory-adaptive Depth-wise Heterogeneous Federated Learning, https://arxiv.org/abs/2303.04887
• I-Cheng Lin, Osman Yagan, Carlee Joe-Wong, 2 Sep 2025, FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning, https://arxiv.org/abs/2410.18862
• Mehdi Ben Ghali, Gouenou Coatrieux, Reda Bellafqira, 1 Sep 2025, FL-CLEANER: byzantine and backdoor defense by CLustering Errors of Activation maps in Non-iid fedErated leaRning, https://arxiv.org/abs/2501.12123
• Yanmeng Wang, Wenkai Ji, Jian Zhou, Fu Xiao, Tsung-Hui Chang, 1 Sep 2025, Robust Federated Learning in Unreliable Wireless Networks: A Client Selection Approach, https://arxiv.org/abs/2502.17260
• Kaoru Otsuka, Yuki Takezawa, Makoto Yamada, 3 Sep 2025, Delayed Momentum Aggregation: Communication-efficient Byzantine-robust Federated Learning with Partial Participation, https://arxiv.org/abs/2509.02970
• Yuhang Yao, Yuan Li, Xinyi Fan, Junhao Li, Kay Liu, Weizhao Jin, Yu Yang, Srivatsan Ravi, Philip S. Yu, Carlee Joe-Wong, 2 Sep 2025, FedGraph: A Research Library and Benchmark for Federated Graph Learning, https://arxiv.org/abs/2410.06340
• Royson Lee, Minyoung Kim, Fady Rezk, Rui Li, Stylianos I. Venieris, Timothy Hospedales, 3 Sep 2025, FedP$^2$EFT: Federated Learning to Personalize PEFT for Multilingual LLMs, https://arxiv.org/abs/2502.04387
• Ismail Hossain, Sai Puppala, Sajedul Talukder, Md Jahangir Alam, 4 Sep 2025, AI-in-the-Loop: Privacy Preserving Real-Time Scam Detection and Conversational Scambaiting by Leveraging LLMs and Federated Learning, https://arxiv.org/abs/2509.05362
• Johan Andreas Balle Rubak, Khuram Naveed, Sanyam Jain, Lukas Esterle, Alexandros Iosifidis and Ruben Pauwels, 8 Sep 2025, Impact of Labeling Inaccuracy and Image Noise on Tooth Segmentation in Panoramic Radiographs using Federated, Centralized and Local Learning, https://arxiv.org/abs/2509.06553
• Vasilis Siomos, Jonathan Passerat-Palmbach, Giacomo Tarroni, 8 Sep 2025, An Architecture Built for Federated Learning: Addressing Data Heterogeneity through Adaptive Normalization-Free Feature Recalibration, https://arxiv.org/abs/2410.02006
• Wenhan Dong, Chao Lin, Xinlei He, Shengmin Xu, Xinyi Huang, 6 Sep 2025, Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks, https://arxiv.org/abs/2412.01650
• Usama Zafar and Andr\'e Teixeira and Salman Toor, 8 Sep 2025, Byzantine-Robust Federated Learning Using Generative Adversarial Networks, https://arxiv.org/abs/2503.20884
• Yiyue Chen, Usman Akram, Chianing Wang, Haris Vikalo, 8 Sep 2025, Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data, https://arxiv.org/abs/2509.07198
• Yuxuan Bai, Yuxuan Sun, Tan Chen, Wei Chen, Sheng Zhou, Zhisheng Niu, 9 Sep 2025, FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning, https://arxiv.org/abs/2509.07342
• Yanxin Yang, Ming Hu, Xiaofei Xie, Yue Cao, Pengyu Zhang, Yihao Huang, Mingsong Chen, 9 Sep 2025, FilterFL: Knowledge Filtering-based Data-Free Backdoor Defense for Federated Learning, https://arxiv.org/abs/2308.11333
• Ozgu Goksu, Nicolas Pugeault, 9 Sep 2025, Hybrid-Regularized Magnitude Pruning for Robust Federated Learning under Covariate Shift, https://arxiv.org/abs/2412.15010
• Mohammad Hasan Narimani and Mostafa Tavassolipour, 12 Sep 2025, FedRP: A Communication-Efficient Approach for Differentially Private Federated Learning Using Random Projection, https://arxiv.org/abs/2509.10041
• Nour Jamoussi, Giuseppe Serra, Photios A. Stavrou, Marios Kountouris, 12 Sep 2025, Cost-Free Personalization via Information-Geometric Projection in Bayesian Federated Learning, https://arxiv.org/abs/2509.10132
• Shiwei Li, Qunwei Li, Haozhao Wang, Ruixuan Li, Jianbin Lin, Wenliang Zhong, 12 Sep 2025, FedBiF: Communication-Efficient Federated Learning via Bits Freezing, https://arxiv.org/abs/2509.10161
• Francisco Javier Esono Nkulu Andong and Qi Min, 12 Sep 2025, Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks, https://arxiv.org/abs/2509.10163
• Teresa Salazar and Jo\~ao Gama and Helder Ara\'ujo and Pedro Henriques Abreu, 12 Sep 2025, Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning, https://arxiv.org/abs/2402.07586
• Teresa Salazar, Helder Ara\'ujo, Alberto Cano, Pedro Henriques Abreu, 12 Sep 2025, A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future Research, https://arxiv.org/abs/2410.03855
• Zeyneddin Oz, Shreyas Korde, Marius Bock, Kristof Van Laerhoven, 12 Sep 2025, FedFitTech: A Baseline in Federated Learning for Fitness Tracking, https://arxiv.org/abs/2506.16840
• Daniel Richards Arputharaj, Charlotte Rodriguez, Angelo Rodio, Giovanni Neglia, 10 Sep 2025, Green Federated Learning via Carbon-Aware Client and Time Slot Scheduling, https://arxiv.org/abs/2509.08980
• Sena Ergisi, Luis Ma{\ss}ny, Rawad Bitar, 11 Sep 2025, ProDiGy: Proximity- and Dissimilarity-Based Byzantine-Robust Federated Learning, https://arxiv.org/abs/2509.09534
• Diying Yang, Yingwei Hou, Weigang Wu, 11 Sep 2025, Convergence Analysis of Asynchronous Federated Learning with Gradient Compression for Non-Convex Optimization, https://arxiv.org/abs/2504.19903
• Shun Takagi, Satoshi Hasegawa, 11 Sep 2025, Securing Private Federated Learning in a Malicious Setting: A Scalable TEE-Based Approach with Client Auditing, https://arxiv.org/abs/2509.08709
• Van-Tuan Tran, Hong-Hanh Nguyen-Le, Quoc-Viet Pham, 19 Sep 2025, ToFU: Transforming How Federated Learning Systems Forget User Data, https://arxiv.org/abs/2509.15861
• Kristina P. Sinaga, 19 Sep 2025, Personalized Federated Learning with Heat-Kernel Enhanced Tensorized Multi-View Clustering, https://arxiv.org/abs/2509.16101
• Rasil Baidar, Sasa Maric, Robert Abbas, 19 Sep 2025, Hybrid Deep Learning-Federated Learning Powered Intrusion Detection System for IoT/5G Advanced Edge Computing Network, https://arxiv.org/abs/2509.15555
• Xiumei Deng, Jun Li, Kang Wei, Long Shi, Zehui Xiong, Ming Ding, Wen Chen, Shi Jin, and H. Vincent Poor, 19 Sep 2025, Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization, https://arxiv.org/abs/2405.17932
• Qiyue Li, Yingxin Liu, Hang Qi, Jieping Luo, Zhizhang Liu, Jingjin Wu, 19 Sep 2025, Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learning, https://arxiv.org/abs/2509.13933
• Binquan Guo, Junteng Cao, Marie Siew, Binbin Chen, Tony Q. S. Quek, Zhu Han, 5 Sep 2025, Accelerating Privacy-Preserving Federated Learning in Large-Scale LEO Satellite Systems, https://arxiv.org/abs/2509.12222
• Ritesh Janga and Rushit Dave, 15 Sep 2025, Enhancing Smart Farming Through Federated Learning: A Secure, Scalable, and Efficient Approach for AI-Driven Agriculture, https://arxiv.org/abs/2509.12363
• Haozhi Shi, Weiying Xie, Hangyu Ye, Daixun Li, Jitao Ma, and Leyuan Fang, 16 Sep 2025, High-Energy Concentration for Federated Learning in Frequency Domain, https://arxiv.org/abs/2509.12630
• Wilfrid Sougrinoma Compaor\'e, Yaya Etiabi, El Mehdi Amhoud, Mohamad Assaad, 16 Sep 2025, Energy-Efficient Quantized Federated Learning for Resource-constrained IoT devices, https://arxiv.org/abs/2509.12814
• Honghong Zeng, Jiong Lou, Zhe Wang, Hefeng Zhou, Chentao Wu, Wei Zhao, Jie Li, 16 Sep 2025, BAPFL: Exploring Backdoor Attacks Against Prototype-based Federated Learning, https://arxiv.org/abs/2509.12964
• Jiahao Xu, Zikai Zhang, Rui Hu, 16 Sep 2025, On the Out-of-Distribution Backdoor Attack for Federated Learning, https://arxiv.org/abs/2509.13219
• Saptarshi Chakraborty and Peter L. Bartlett, 16 Sep 2025, A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional Data, https://arxiv.org/abs/2410.20659
• Jiaxing Cao, Yuzhou Gao, Jiwei Huang, 3 Sep 2025, A Service-Oriented Adaptive Hierarchical Incentive Mechanism for Federated Learning, https://arxiv.org/abs/2509.10512
• Rodrigo Tertulino, 3 Sep 2025, A Comparative Benchmark of Federated Learning Strategies for Mortality Prediction on Heterogeneous and Imbalanced Clinical Data, https://arxiv.org/abs/2509.10517
• Sahil Tyagi, 5 Sep 2025, On Using Large-Batches in Federated Learning, https://arxiv.org/abs/2509.10537
• Ali Burak \"Unal, Cem Ata Baykara, Peter Krawitz, Mete Akg\"un, 12 Sep 2025, Accurate and Private Diagnosis of Rare Genetic Syndromes from Facial Images with Federated Deep Learning, https://arxiv.org/abs/2509.10635
• Fardin Jalil Piran, Zhiling Chen, Yang Zhang, Qianyu Zhou, Jiong Tang, Farhad Imani, 12 Sep 2025, Privacy-Preserving Decentralized Federated Learning via Explainable Adaptive Differential Privacy, https://arxiv.org/abs/2509.10691
• Soumia Zohra El Mestari, Maciej Krzysztof Zuziak and Gabriele Lenzini, 15 Sep 2025, Poison to Detect: Detection of Targeted Overfitting in Federated Learning, https://arxiv.org/abs/2509.11974
• Cosimo Fiorini, Matteo Mosconi, Pietro Buzzega, Riccardo Salami, Simone Calderara, 15 Sep 2025, Intrinsic Training Signals for Federated Learning Aggregation, https://arxiv.org/abs/2507.06813
• Herlock (SeyedAbolfazl) Rahimi, Dionysis Kalogerias, 17 Sep 2025, FedAVOT: Exact Distribution Alignment in Federated Learning via Masked Optimal Transport, https://arxiv.org/abs/2509.14444
• Xingchen Wang, Feijie Wu, Chenglin Miao, Tianchun Li, Haoyu Hu, Qiming Cao, Jing Gao, Lu Su, 18 Sep 2025, Towards Privacy-Preserving and Heterogeneity-aware Split Federated Learning via Probabilistic Masking, https://arxiv.org/abs/2509.14603
• Zeyu Chen, Wen Chen, Jun Li, Qingqing Wu, Ming Ding, Xuefeng Han, Xiumei Deng, Liwei Wang, 18 Sep 2025, Hierarchical Federated Learning for Social Network with Mobility, https://arxiv.org/abs/2509.14938
• Viktor Kovalchuk, Nikita Kotelevskii, Maxim Panov, Samuel Horv\'ath, Martin Tak\'a\v{c}, 18 Sep 2025, Who to Trust? Aggregating Client Knowledge in Logit-Based Federated Learning, https://arxiv.org/abs/2509.15147
• Linfeng Luo, Zhiqi Guo, Fengxiao Tang, Zihao Qiu, Ming Zhao, 18 Sep 2025, Federated Hypergraph Learning with Local Differential Privacy: Toward Privacy-Aware Hypergraph Structure Completion, https://arxiv.org/abs/2408.05160
• Chih Wei Ling, Chun Hei Michael Shiu, Youqi Wu, Jiande Sun, Cheuk Ting Li, Linqi Song, Weitao Xu, 18 Sep 2025, Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning, https://arxiv.org/abs/2501.12046
• Zhihao Wang, Wenke Huang, Tian Chen, Zekun Shi, Guancheng Wan, Yu Qiao, Bin Yang, Jian Wang, Bing Li, Mang Ye, 18 Sep 2025, An Empirical Study of Federated Prompt Learning for Vision Language Model, https://arxiv.org/abs/2505.23024
• Haochen Zhang, Zhong Zheng, Lingzhou Xue, 18 Sep 2025, Gap-Dependent Bounds for Federated $Q$-learning, https://arxiv.org/abs/2502.02859
• Lucas Fenaux, Zheng Wang, Jacob Yan, Nathan Chung, Florian Kerschbaum, 9 Sep 2025, Hammer and Anvil: A Principled Defense Against Backdoors in Federated Learning, https://arxiv.org/abs/2509.08089
• Konstantin Burlachenko, 9 Sep 2025, Optimization Methods and Software for Federated Learning, https://arxiv.org/abs/2509.08120
• Qiaobo Li, Zhijie Chen, Arindam Banerjee, 9 Sep 2025, Sketched Gaussian Mechanism for Private Federated Learning, https://arxiv.org/abs/2509.08195
• Kai Yi, 10 Sep 2025, Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization, https://arxiv.org/abs/2509.08233
• Delio Jaramillo-Velez and Charul Rajput and Ragnar Freij-Hollanti and Camilla Hollanti and Alexandre Graell i Amat, 10 Sep 2025, Perfectly-Private Analog Secure Aggregation in Federated Learning, https://arxiv.org/abs/2509.08683
• Yuanchun Guo and Bingyan Liu and Yulong Sha and Zhensheng Xian, 4 Sep 2025, PracMHBench: Re-evaluating Model-Heterogeneous Federated Learning Based on Practical Edge Device Constraints, https://arxiv.org/abs/2509.08750
• Charuka Herath, Yogachandran Rahulamathavan, Varuna De Silva, and Sangarapillai Lambotharan, 10 Sep 2025, DSFL: A Dual-Server Byzantine-Resilient Federated Learning Framework via Group-Based Secure Aggregation, https://arxiv.org/abs/2509.08449
• Avais Jan, Qasim Zia, Murray Patterson, 9 Sep 2025, Enhancing Privacy Preservation and Reducing Analysis Time with Federated Transfer Learning in Digital Twins-based Computed Tomography Scan Analysis, https://arxiv.org/abs/2509.08018
• Yuyang Zhou, Guang Cheng, Kang Du, Zihan Chen, Tian Qin, Yuyu Zhao, 10 Sep 2025, From Static to Adaptive Defense: Federated Multi-Agent Deep Reinforcement Learning-Driven Moving Target Defense Against DoS Attacks in UAV Swarm Networks, https://arxiv.org/abs/2506.07392
• Yuyang Qiu, Kibaek Kim, Farzad Yousefian, 10 Sep 2025, A Randomized Zeroth-Order Hierarchical Framework for Heterogeneous Federated Learning, https://arxiv.org/abs/2504.01839
• Md Bokhtiar Al Zami, Md Raihan Uddin, and Dinh C. Nguyen, 17 Sep 2025, Secure UAV-assisted Federated Learning: A Digital Twin-Driven Approach with Zero-Knowledge Proofs, https://arxiv.org/abs/2509.13634
• Zihou Wu (1), Yuecheng Li (1), Tianchi Liao (2), Jian Lou (2), Chuan Chen (1) ((1) School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China (2) School of Software Engineering, Sun Yat-sen University, Zhuhai, China), 17 Sep 2025, ParaAegis: Parallel Protection for Flexible Privacy-preserved Federated Learning, https://arxiv.org/abs/2509.13739
• Zhanting Zhou and Jinshan Lai and Fengchun Zhang and Zeqin Wu and Fengli Zhang, 17 Sep 2025, FedSSG: Expectation-Gated and History-Aware Drift Alignment for Federated Learning, https://arxiv.org/abs/2509.13895
• Raouf Kerkouche, Henrik Zunker, Mario Fritz, Martin J. K\"uhn, 17 Sep 2025, Differentially private federated learning for localized control of infectious disease dynamics, https://arxiv.org/abs/2509.14024
• Ozer Ozturk, Busra Buyuktanir, Gozde Karatas Baydogmus, Kazim Yildiz, 17 Sep 2025, Differential Privacy in Federated Learning: Mitigating Inference Attacks with Randomized Response, https://arxiv.org/abs/2509.13987
• Chenghao Huang, Xiaolu Chen, Yanru Zhang, and Hao Wang, 17 Sep 2025, FedCoSR: Personalized Federated Learning with Contrastive Shareable Representations for Label Heterogeneity in Non-IID Data, https://arxiv.org/abs/2404.17916
• Youngjoon Lee, Jinu Gong, Joonhyuk Kang, 17 Sep 2025, Embedding Byzantine Fault Tolerance into Federated Learning via Consistency Scoring, https://arxiv.org/abs/2411.10212
• Gergely D. N\'emeth, Eros Fan\`i, Yeat Jeng Ng, Barbara Caputo, Miguel \'Angel Lozano, Nuria Oliver, Novi Quadrianto, 16 Sep 2025, FedDiverse: Tackling Data Heterogeneity in Federated Learning with Diversity-Driven Client Selection, https://arxiv.org/abs/2504.11216
• Youngjoon Lee, Jinu Gong, Joonhyuk Kang, 17 Sep 2025, A Unified Benchmark of Federated Learning with Kolmogorov-Arnold Networks for Medical Imaging, https://arxiv.org/abs/2504.19639
• Vedant Palit, 25 Sep 2025, Adaptive Federated Learning Defences via Trust-Aware Deep Q-Networks, https://arxiv.org/abs/2510.01261
• Shaba Shaon and Dinh C. Nguyen, 2 Oct 2025, Latency-aware Multimodal Federated Learning over UAV Networks, https://arxiv.org/abs/2510.01717
• Jie Fu, Yuan Hong, Xinpeng Ling, Leixia Wang, Xun Ran, Zhiyu Sun, Wendy Hui Wang, Zhili Chen, and Yang Cao, 2 Oct 2025, Differentially Private Federated Learning: A Systematic Review, https://arxiv.org/abs/2405.08299
• Harsh Kasyap, Minghong Fang, Zhuqing Liu, Carsten Maple, Somanath Tripathy, 14 Oct 2025, Fairness-Constrained Optimization Attack in Federated Learning, https://arxiv.org/abs/2510.12143
• Ningxin He, Yang Liu, Wei Sun, Xiaozhou Ye, Ye Ouyang, Tiegang Gao, Zehui Zhang, 14 Oct 2025, FedMMKT:Co-Enhancing a Server Text-to-Image Model and Client Task Models in Multi-Modal Federated Learning, https://arxiv.org/abs/2510.12254
• Kevin Kuo, Chhavi Yadav, Virginia Smith, 14 Oct 2025, Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice, https://arxiv.org/abs/2510.12595
• Anas Abouaomar, Mohammed El hanjri, Abdellatif Kobbane, Anis Laouiti, Khalid Nafil, 14 Oct 2025, Hierarchical Federated Learning for Crop Yield Prediction in Smart Agricultural Production Systems, https://arxiv.org/abs/2510.12727
• Felix Marx, Thomas Schneider, Ajith Suresh, Tobias Wehrle, Christian Weinert, Hossein Yalame, 14 Oct 2025, WW-FL: Secure and Private Large-Scale Federated Learning, https://arxiv.org/abs/2302.09904
• Yuqi Jia, Minghong Fang, Neil Zhenqiang Gong, 14 Oct 2025, Competitive Advantage Attacks to Decentralized Federated Learning, https://arxiv.org/abs/2310.13862
• Yichen Li, Yuying Wang, Jiahua Dong, Haozhao Wang, Yining Qi, Rui Zhang, Ruixuan Li, 14 Oct 2025, Resource-Constrained Federated Continual Learning: What Does Matter?, https://arxiv.org/abs/2501.08737
• Saida Elouardi, Mohammed Jouhari, Anas Motii, 13 Oct 2025, OptiFLIDS: Optimized Federated Learning for Energy-Efficient Intrusion Detection in IoT, https://arxiv.org/abs/2510.05180
• Abdelrhman Gaber, Hassan Abd-Eltawab, Youssif Abuzied, Muhammad ElMahdy, Tamer ElBatt, 29 Sep 2025, Federated Learning Meets LLMs: Feature Extraction From Heterogeneous Clients, https://arxiv.org/abs/2510.00065
• Kassahun Azezew, Minyechil Alehegn, Tsega Asresa, Bitew Mekuria, Tizazu Bayh, Ayenew Kassie, Amsalu Tesema, Animut Embiyale, 1 Oct 2025, Privacy Preserved Federated Learning with Attention-Based Aggregation for Biometric Recognition, https://arxiv.org/abs/2510.01113
• Huy Q. Le, Ye Lin Tun, Yu Qiao, Minh N. H. Nguyen, Keon Oh Kim, Eui-Nam Huh, Choong Seon Hong, 1 Oct 2025, Mitigating Domain Shift in Federated Learning via Intra- and Inter-Domain Prototypes, https://arxiv.org/abs/2501.08521
• Thien Pham, Angelo Furno, Fa\"icel Chamroukhi, Latifa Oukhellou, 1 Oct 2025, Federated Dynamic Modeling and Learning for Spatiotemporal Data Forecasting, https://arxiv.org/abs/2503.04528
• Sahil Tyagi, Andrei Cozma, Olivera Kotevska, Feiyi Wang, 23 Sep 2025, OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPC, https://arxiv.org/abs/2509.19396
• Kunlun Xu and Yibo Feng and Jiangmeng Li and Yongsheng Qi and Jiahuan Zhou, 24 Sep 2025, C${}^2$Prompt: Class-aware Client Knowledge Interaction for Federated Continual Learning, https://arxiv.org/abs/2509.19674
• Balazs Pejo and Marcell Frank and Krisztian Varga and Peter Veliczky, 24 Sep 2025, On the Fragility of Contribution Score Computation in Federated Learning, https://arxiv.org/abs/2509.19921
• Fahmida Islam, Adnan Mahmood, Noorain Mukhtiar, Kasun Eranda Wijethilake, Quan Z. Sheng, 24 Sep 2025, FairEquityFL -- A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV Networks, https://arxiv.org/abs/2509.20193
• Tong Cheng, Jie Fu, Xinpeng Ling, Huifa Li, Zhili Chen, Haifeng Qian, Junqing Gong, 24 Sep 2025, EC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compression, https://arxiv.org/abs/2505.15140
• Ganyu Wang, Jinjie Fang, Maxwell J. Yin, Bin Gu, Xi Chen, Boyu Wang, Yi Chang, Charles Ling, 23 Sep 2025, FedOne: Query-Efficient Federated Learning for Black-box Discrete Prompt Learning, https://arxiv.org/abs/2506.14929
• Miguel Fernandez-de-Retana, Unai Zulaika, Rub\'en S\'anchez-Corcuera, Aitor Almeida, 27 Oct 2025, Differential Privacy: Gradient Leakage Attacks in Federated Learning Environments, https://arxiv.org/abs/2510.23931
• Mortesa Hussaini, Jan Thei{\ss} and Anthony Stein, 28 Oct 2025, Local Performance vs. Out-of-Distribution Generalization: An Empirical Analysis of Personalized Federated Learning in Heterogeneous Data Environments, https://arxiv.org/abs/2510.24503
• Fan Zhang, Daniel Kreuter, Carlos Esteve-Yag\"ue, S\"oren Dittmer, Javier Fernandez-Marques, Samantha Ip, BloodCounts! Consortium, Norbert C.J. de Wit, Angela Wood, James HF Rudd, Nicholas Lane, Nicholas S Gleadall, Carola-Bibiane Sch\"onlieb, and Michael Roberts, 28 Oct 2025, FedMAP: Personalised Federated Learning for Real Large-Scale Healthcare Systems, https://arxiv.org/abs/2405.19000
• Peilin He, James Joshi, 27 Oct 2025, PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction, https://arxiv.org/abs/2507.00230
• Khoa Nguyen, Khang Tran, NhatHai Phan, Cristian Borcea, Rouming Jin, Issa Khalil, 28 Oct 2025, SGFusion: Stochastic Geographic Gradient Fusion in Federated Learning, https://arxiv.org/abs/2510.23455
• Xiang Li, Buxin Su, Chendi Wang, Qi Long, Weijie J. Su, 22 Oct 2025, Mitigating Privacy-Utility Trade-off in Decentralized Federated Learning via $f$-Differential Privacy, https://arxiv.org/abs/2510.19934
• Ke Xing, Yanjie Dong, Xiaoyi Fan, Runhao Zeng, Victor C. M. Leung, M. Jamal Deen and Xiping Hu, 23 Oct 2025, CO-PFL: Contribution-Oriented Personalized Federated Learning for Heterogeneous Networks, https://arxiv.org/abs/2510.20219
• Insu Jeon, Minui Hong, Junhyeog Yun, Gunhee Kim, 23 Oct 2025, Federated Learning via Meta-Variational Dropout, https://arxiv.org/abs/2510.20225
• Zhiqin Yang, Yonggang Zhang, Chenxin Li, Yiu-ming Cheung, Bo Han, Yixuan Yuan, 23 Oct 2025, FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning, https://arxiv.org/abs/2510.20250
• Tan-Khiem Huynh, Malcolm Egan, Giovanni Neglia, Jean-Marie Gorce, 23 Oct 2025, Streaming Federated Learning with Markovian Data, https://arxiv.org/abs/2503.18807
• Vincenzo Carletti, Pasquale Foggia, Carlo Mazzocca, Giuseppe Parrella, Mario Vento, 23 Oct 2025, GUIDE: Enhancing Gradient Inversion Attacks in Federated Learning with Denoising Models, https://arxiv.org/abs/2510.17621
• Soham Bonnerjee, Sayar Karmakar, Wei Biao Wu, 22 Oct 2025, Sharp Gaussian approximations for Decentralized Federated Learning, https://arxiv.org/abs/2505.08125
• Jahidul Arafat, Fariha Tasmin, Sanjaya Poudel, Iftekhar Haider, 23 Oct 2025, Beyond Static Knowledge Messengers: Towards Adaptive, Fair, and Scalable Federated Learning for Medical AI, https://arxiv.org/abs/2510.06259
• Lunchen Xie, Zehua He, Qingjiang Shi, 17 Oct 2025, FedPURIN: Programmed Update and Reduced INformation for Sparse Personalized Federated Learning, https://arxiv.org/abs/2510.16065
• Jaehan Kim, Minkyoo Song, Minjae Seo, Youngjin Jin, Seungwon Shin, Jinwoo Kim, 17 Oct 2025, PassREfinder-FL: Privacy-Preserving Credential Stuffing Risk Prediction via Graph-Based Federated Learning for Representing Password Reuse between Websites, https://arxiv.org/abs/2510.16083
• Anthony DiMaggio, Raghav Sharma, Gururaj Saileshwar, 19 Oct 2025, CLIP: Client-Side Invariant Pruning for Mitigating Stragglers in Secure Federated Learning, https://arxiv.org/abs/2510.16694
• Ludi Li, Junbin Mao, Hanhe Lin, Xu Tian, Fang-Xiang Wu, Jin Liu, 20 Oct 2025, CEPerFed: Communication-Efficient Personalized Federated Learning for Multi-Pulse MRI Classification, https://arxiv.org/abs/2510.17584
• S\'ebastien Thuau, Siba Haidar, Ayush Bajracharya, Rachid Chelouah, 20 Oct 2025, Frugal Federated Learning for Violence Detection: A Comparison of LoRA-Tuned VLMs and Personalized CNNs, https://arxiv.org/abs/2510.17651
• Siva Sai, Abhishek Sawaika, Prabhjot Singh and Rajkumar Buyya, 20 Oct 2025, Quantum Federated Learning: Architectural Elements and Future Directions, https://arxiv.org/abs/2510.17642
• Zhenwei Huang, Wen Huang, Pratik Jawanpuria, Bamdev Mishra, 19 Oct 2025, Riemannian Federated Learning via Averaging Gradient Streams, https://arxiv.org/abs/2409.07223
• Dun Zeng, Zheshun Wu, Shiyu Liu, Yu Pan, Xiaoying Tang, Zenglin Xu, 19 Oct 2025, Understanding Generalization of Federated Learning: the Trade-off between Model Stability and Optimization, https://arxiv.org/abs/2411.16303
• Runlin Zhou and Letian Li and Zemin Zheng, 19 Oct 2025, Row-wise Fusion Regularization: An Interpretable Personalized Federated Learning Framework in Large-Scale Scenarios, https://arxiv.org/abs/2510.14413
• Manuel Noseda, Alberto De Luca, Lukas Von Briel, Nathan Lacour, 19 Sep 2025, Federated Learning for Financial Forecasting, https://arxiv.org/abs/2509.16393
• Marijan Fofonjka, Shahryar Zehtabi, Alireza Behtash, Tyler Mauer and David Stout, 20 Sep 2025, Federated Learning with Ad-hoc Adapter Insertions: The Case of Soft-Embeddings for Training Classifier-as-Retriever, https://arxiv.org/abs/2509.16508
• Antonio Tarizzo, Mohammad Kazemi, Deniz G\"und\"uz, 20 Sep 2025, Learned Digital Codes for Over-the-Air Federated Learning, https://arxiv.org/abs/2509.16577
• Letian Zhang, Bo Chen, Jieming Bian, Lei Wang, Jie Xu, 21 Sep 2025, FedEL: Federated Elastic Learning for Heterogeneous Devices, https://arxiv.org/abs/2509.16902
• Saeid Sheikhi, Panos Kostakos, Lauri Loven, 22 Sep 2025, Hybrid Reputation Aggregation: A Robust Defense Mechanism for Adversarial Federated Learning in 5G and Edge Network Environments, https://arxiv.org/abs/2509.18044
• Dev Gurung, Shiva Raj Pokhrel, 20 Sep 2025, orb-QFL: Orbital Quantum Federated Learning, https://arxiv.org/abs/2509.16505
• Muxing Wang, Pengkun Yang, Lili Su, 20 Sep 2025, On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments, https://arxiv.org/abs/2409.03897
• Ali Forootani, Raffaele Iervolino, 22 Sep 2025, Asynchronous Federated Learning: A Scalable Approach for Decentralized Machine Learning, https://arxiv.org/abs/2412.17723
• Minghong Wu, Minghui Liwang, Yuhan Su, Li Li, Seyyedali Hosseinalipour, Xianbin Wang, Huaiyu Dai, Zhenzhen Jiao, 21 Sep 2025, Towards Seamless Hierarchical Federated Learning under Intermittent Client Participation: A Stagewise Decision-Making Methodology, https://arxiv.org/abs/2502.09303
• Sajid Hussain, Muhammad Sohail, Nauman Ali Khan, Naima Iltaf, and Ihtesham ul Islam, 21 Sep 2025, Progressive Size-Adaptive Federated Learning: A Comprehensive Framework for Heterogeneous Multi-Modal Data Systems, https://arxiv.org/abs/2506.20685
• Mohammadsajad Alipour, Mohammad Mohammadi Amiri, 25 Oct 2025, Power to the Clients: Federated Learning in a Dictatorship Setting, https://arxiv.org/abs/2510.22149
• Duong M. Nguyen, Trong Nghia Hoang, Thanh Trung Huynh, Quoc Viet Hung Nguyen, Phi Le Nguyen, 27 Oct 2025, Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Data, https://arxiv.org/abs/2510.22880
• Zhiyu Wang, Suman Raj, Rajkumar Buyya, 27 Oct 2025, AirFed: Federated Graph-Enhanced Multi-Agent Reinforcement Learning for Multi-UAV Cooperative Mobile Edge Computing, https://arxiv.org/abs/2510.23053
• Hao Liang, Haifeng Wen, Kaishun Wu, Hong Xing, 27 Oct 2025, Differential Privacy as a Perk: Federated Learning over Multiple-Access Fading Channels with a Multi-Antenna Base Station, https://arxiv.org/abs/2510.23463
• Aladin Djuhera, Vlad C. Andrei, Xinyang Li, Ullrich J. M\"onich, Holger Boche, Walid Saad, 27 Oct 2025, R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Models, https://arxiv.org/abs/2407.11654
• Shiyuan Zuo, Xingrun Yan, Rongfei Fan, Li Shen, Puning Zhao, Jie Xu, Han Hu, 25 Oct 2025, Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients, https://arxiv.org/abs/2408.09539
• Geetika, Somya Tyagi, Bapi Chatterjee, 26 Oct 2025, Painless Federated Learning: An Interplay of Line-Search and Extrapolation, https://arxiv.org/abs/2408.17145
• Seanie Lee, Sangwoo Park, Dong Bok Lee, Dominik Wagner, Haebin Seong, Tobias Bocklet, Juho Lee, Sung Ju Hwang, 25 Oct 2025, FedSVD: Adaptive Orthogonalization for Private Federated Learning with LoRA, https://arxiv.org/abs/2505.12805
• Payam Abdisarabshali, Kwang Taik Kim, Michael Langberg, Weifeng Su, Seyyedali Hosseinalipour, 25 Oct 2025, Dynamic D2D-Assisted Federated Learning over O-RAN: Performance Analysis, MAC Scheduler, and Asymmetric User Selection, https://arxiv.org/abs/2404.06324
• Haolin Li, Hoda Bidkhori, 14 Oct 2025, FedGTEA: Federated Class-Incremental Learning with Gaussian Task Embedding and Alignment, https://arxiv.org/abs/2510.12927
• Jieping Luo, Qiyue Li, Zhizhang Liu, Hang Qi, Jiaying Yin, Jingjin Wu, 15 Oct 2025, Cluster-Based Client Selection for Dependent Multi-Task Federated Learning in Edge Computing, https://arxiv.org/abs/2510.13132
• Alejandro Guerra-Manzanares, Omar El-Herraoui, Michail Maniatakos and Farah E. Shamout, 15 Oct 2025, BlendFL: Blended Federated Learning for Handling Multimodal Data Heterogeneity, https://arxiv.org/abs/2510.13266
• Omayma Moussadek, Riccardo Salami and Simone Calderara, 15 Oct 2025, DOLFIN: Balancing Stability and Plasticity in Federated Continual Learning, https://arxiv.org/abs/2510.13567
• Riccardo Santi, Riccardo Salami and Simone Calderara, 15 Oct 2025, Towards Robust Knowledge Removal in Federated Learning with High Data Heterogeneity, https://arxiv.org/abs/2510.13606
• Alessandro Licciardi, Roberta Raineri, Anton Proskurnikov, Lamberto Rondoni, Lorenzo Zino, 14 Oct 2025, Socially inspired Adaptive Coalition and Client Selection in Federated Learning, https://arxiv.org/abs/2506.02897
• Seohyeon Cha, Huancheng Chen, Haris Vikalo, 25 Sep 2025, Task-Agnostic Federated Continual Learning via Replay-Free Gradient Projection, https://arxiv.org/abs/2509.21606
• Weiqi Yue, Wenbiao Li, Yuzhou Jiang, Anisa Halimi, Roger French, Erman Ayday, 25 Sep 2025, PQFed: A Privacy-Preserving Quality-Controlled Federated Learning Framework, https://arxiv.org/abs/2509.21704
• Li Xia, Zheng Liu, Sili Huang, Wei Tang, Xuan Liu, 26 Sep 2025, Non-Linear Trajectory Modeling for Multi-Step Gradient Inversion Attacks in Federated Learning, https://arxiv.org/abs/2509.22082
• Bo Wang, Imran Khan, Martin White, Natalia Beloff, 26 Sep 2025, Role-Aware Multi-modal federated learning system for detecting phishing webpages, https://arxiv.org/abs/2509.22369
• Zahid Iqbal, 26 Sep 2025, Adaptive Dual-Mode Distillation with Incentive Schemes for Scalable, Heterogeneous Federated Learning on Non-IID Data, https://arxiv.org/abs/2509.22507
• Devashish Chaudhary, Sutharshan Rajasegarar and Shiva Raj Pokhrel, 24 Sep 2025, Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Survey, https://arxiv.org/abs/2509.21389
• Amr Abourayya, Jens Kleesiek, Bharat Rao, Michael Kamp, 26 Sep 2025, Whom to Trust? Adaptive Collaboration in Personalized Federated Learning, https://arxiv.org/abs/2507.00259
• Mariona Jaramillo-Civill, Peng Wu, Pau Closas, 8 Oct 2025, DPMM-CFL: Clustered Federated Learning via Dirichlet Process Mixture Model Nonparametric Clustering, https://arxiv.org/abs/2510.07132
• Jason Han, Nicholas S. DiBrita, Daniel Leeds, Jianqiang Li, Jason Ludmir and Tirthak Patel, 30 Sep 2025, Layerwise Federated Learning for Heterogeneous Quantum Clients using Quorus, https://arxiv.org/abs/2510.06228
• Jiarui Song, Yunheng Shen, Chengbin Hou, Pengyu Wang, Jinbao Wang, Ke Tang, Hairong Lv, 8 Oct 2025, FedAGHN: Personalized Federated Learning with Attentive Graph HyperNetworks, https://arxiv.org/abs/2501.16379
• Haoran Gao, Samuel D. Okegbile, and Jun Cai, 7 Oct 2025, A Novel Collaborative Framework for Efficient Synchronization in Split Federated Learning over Wireless Networks, https://arxiv.org/abs/2503.15559
• Tharuka Kasthuri Arachchige, Veselka Boeva and Shahrooz Abghari, 3 Oct 2025, FeDABoost: Fairness Aware Federated Learning with Adaptive Boosting, https://arxiv.org/abs/2510.02914
• Irene Tenison, Anna Murphy, Charles Beauville, Lalana Kagal, 3 Oct 2025, FTTE: Federated Learning on Resource-Constrained Devices, https://arxiv.org/abs/2510.03165
• Bochra Al Agha, Razane Tajeddine, 29 Sep 2025, Federated Spatiotemporal Graph Learning for Passive Attack Detection in Smart Grids, https://arxiv.org/abs/2510.02371
• Chao Feng, Nicolas Fazli Kohler, Zhi Wang, Weijie Niu, Alberto Huertas Celdran, Gerome Bovet, Burkhard Stiller, 3 Oct 2025, ColNet: Collaborative Optimization in Decentralized Federated Multi-task Learning Systems, https://arxiv.org/abs/2501.10347
• Tianyu Zhao, Mahmoud Srewa, Salma Elmalaki, 2 Oct 2025, FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk, https://arxiv.org/abs/2502.17748
• Fardis Nadimi, Payam Abdisarabshali, Jacob Chakareski, Nicholas Mastronarde, Seyyedali Hosseinalipour, 2 Oct 2025, Graph Theory Meets Federated Learning over Satellite Constellations: Spanning Aggregations, Network Formation, and Performance Optimization, https://arxiv.org/abs/2509.24932
• Alex Acero, Daniel M. Jimenez-Gutierrez, Dario Pighin, Enrique Zuazua, Joaquin Del Rio, Xabi Uribe-Etxebarria, 19 Oct 2025, The Sherpa.ai Blind Vertical Federated Learning Paradigm to Minimize the Number of Communications, https://arxiv.org/abs/2510.17901
• Qian Chen, Xianhao Chen, Kaibin Huang, 21 Oct 2025, FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networks, https://arxiv.org/abs/2412.17231
• Ori Peleg, Natalie Lang, Dan Ben Ami, Stefano Rini, Nir Shlezinger, Kobi Cohen, 21 Oct 2025, PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learning, https://arxiv.org/abs/2503.13173
• Zhuang Qi, Ying-Peng Tang, Lei Meng, Han Yu, Xiaoxiao Li, Xiangxu Meng, 21 Oct 2025, Class-wise Balancing Data Replay for Federated Class-Incremental Learning, https://arxiv.org/abs/2507.07712
• Long Li, Jiajia Li, Dong Chen, Lina Pu, Haibo Yao, Yanbo Huang, 20 Oct 2025, VLLFL: A Vision-Language Model Based Lightweight Federated Learning Framework for Smart Agriculture, https://arxiv.org/abs/2504.13365
• Yipu Zhang, Chengshuo Zhang, Ziyu Zhou, Gang Qu, Hao Zheng, Yuping Wang, Hui Shen, Hongwen Deng, 25 Sep 2025, Personalized Federated Dictionary Learning for Modeling Heterogeneity in Multi-site fMRI Data, https://arxiv.org/abs/2509.20627
• Wenkai Guo, Xuefeng Liu, Haolin Wang, Jianwei Niu, Shaojie Tang, Jing Yuan, 25 Sep 2025, Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation, https://arxiv.org/abs/2509.20680
• Christoph D\"using and Philipp Cimiano, 25 Sep 2025, Distribution-Controlled Client Selection to Improve Federated Learning Strategies, https://arxiv.org/abs/2509.20877
• Christoph D\"using and Philipp Cimiano, 25 Sep 2025, Improving Early Sepsis Onset Prediction Through Federated Learning, https://arxiv.org/abs/2509.20885
• Wei Wan, Yuxuan Ning, Zhicong Huang, Cheng Hong, Shengshan Hu, Ziqi Zhou, Yechao Zhang, Tianqing Zhu, Wanlei Zhou, Leo Yu Zhang, 21 Sep 2025, MARS: A Malignity-Aware Backdoor Defense in Federated Learning, https://arxiv.org/abs/2509.20383
• Amr Akmal Abouelmagd, Amr Hilal, 25 Sep 2025, Emerging Paradigms for Securing Federated Learning Systems, https://arxiv.org/abs/2509.21147
• Shanshan Yan, Zexi Li, Chao Wu, Meng Pang, Yang Lu, Yan Yan, Hanzi Wang, 25 Sep 2025, You Are Your Own Best Teacher: Achieving Centralized-level Performance in Federated Learning under Heterogeneous and Long-tailed Data, https://arxiv.org/abs/2503.06916
• Rahul Atul Bhope, K.R. Jayaram, Praveen Venkateswaran, Nalini Venkatasubramanian, 25 Sep 2025, Shift Happens: Mixture of Experts based Continual Adaptation in Federated Learning, https://arxiv.org/abs/2506.18789
• Zijian Wang, Xiaofei Zhang, Xin Zhang, Yukun Liu, Qiong Zhang, 27 Sep 2025, Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning, https://arxiv.org/abs/2509.23049
• Zhanhong Xie and Meifan Zhang and Lihua Yin, 27 Sep 2025, CoSIFL: Collaborative Secure and Incentivized Federated Learning with Differential Privacy, https://arxiv.org/abs/2509.23190
• Danni Yang, Zhikang Chen, Sen Cui, Mengyue Yang, Ding Li, Abudukelimu Wuerkaixi, Haoxuan Li, Jinke Ren, Mingming Gong, 28 Sep 2025, Decentralized Dynamic Cooperation of Personalized Models for Federated Continual Learning, https://arxiv.org/abs/2509.23683
• Soroosh Safari Loaliyan, Jose-Luis Ambite, Paul M. Thompson, Neda Jahanshad, Greg Ver Steeg, 28 Sep 2025, FedDAPL: Toward Client-Private Generalization in Federated Learning, https://arxiv.org/abs/2509.23688
• Shiyuan Zuo, Rongfei Fan, Cheng Zhan, Jie Xu, Puning Zhao, Han Hu, 29 Sep 2025, H+: An Efficient Similarity-Aware Aggregation for Byzantine Resilient Federated Learning, https://arxiv.org/abs/2509.24330
• Zifan Wang, Xinlei Yi, Xenia Konti, Michael M. Zavlanos, and Karl H. Johansson, 29 Sep 2025, Distributionally Robust Federated Learning with Outlier Resilience, https://arxiv.org/abs/2509.24462
• Ziliang Hong, Halil Ertugrul Aktas, Andrea Mia Bejar, Katherine Wu, Hongyi Pan, Gorkem Durak, Zheyuan Zhang, Sait Kayali, Temel Tirkes, Federica Proietto Salanitri, Concetto Spampinato, Michael Goggins, Tamas Gonda, Candice Bolan, Raj Keswani, Frank Miller, Michael Wallace, Ulas Bagci, 28 Sep 2025, Pancreas Part Segmentation under Federated Learning Paradigm, https://arxiv.org/abs/2509.23562
• Xiangchen Meng, Yangdi Lyu, 27 Sep 2025, FedBit: Accelerating Privacy-Preserving Federated Learning via Bit-Interleaved Packing and Cross-Layer Co-Design, https://arxiv.org/abs/2509.23091
• Shiyuan Zuo, Xingrun Yan, Rongfei Fan, Han Hu, Hangguan Shan, Tony Q. S. Quek, Puning Zhao, 29 Sep 2025, Federated Learning Resilient to Byzantine Attacks and Data Heterogeneity, https://arxiv.org/abs/2403.13374
• Zhengyu Wu, Guang Zeng, Huilin Lai, Daohan Su, Jishuo Jia, Yinlin Zhu, Xunkai Li, Rong-Hua Li, Guoren Wang, Chenghu Zhou, 28 Sep 2025, Toward Model-centric Heterogeneous Federated Graph Learning: A Knowledge-driven Approach, https://arxiv.org/abs/2501.12624
• Xiao Liu, Mingyuan Li, Xu Wang, Guangsheng Yu, Wei Ni, Lixiang Li, Haipeng Peng, Renping Liu, 28 Sep 2025, BlockFUL: Enabling Unlearning in Blockchained Federated Learning, https://arxiv.org/abs/2402.16294
• Aakar Mathur, Ashish Gupta, Sajal K. Das, 28 Sep 2025, When Federated Learning Meets Quantum Computing: Survey and Research Opportunities, https://arxiv.org/abs/2504.08814
• Cade Houston Kennedy, Amr Hilal, Morteza Momeni, 7 Oct 2025, The Role of Federated Learning in Improving Financial Security: A Survey, https://arxiv.org/abs/2510.14991
• Utku Demir, Tugba Erpek, Yalin E. Sagduyu, Sastry Kompella, Mengran Xue, 16 Oct 2025, Targeted Attacks and Defenses for Distributed Federated Learning in Vehicular Networks, https://arxiv.org/abs/2510.15109
• Riccardo Presotto, Gabriele Civitarese, Claudio Bettini, 17 Oct 2025, Personalized Semi-Supervised Federated Learning for Human Activity Recognition, https://arxiv.org/abs/2104.08094
• Chanuka A.S. Hewa Kaluannakkage, Rajkumar Buyya, 17 Oct 2025, Incentive-Based Federated Learning: Architectural Elements and Future Directions, https://arxiv.org/abs/2510.14208
• Dongqi Zheng, Wenjin Fu, 29 Sep 2025, CAFL-L: Constraint-Aware Federated Learning with Lagrangian Dual Optimization for On-Device Language Models, https://arxiv.org/abs/2510.03298
• Michael Ben Ali (IRIT, IRIT-SIG, UT3), Imen Megdiche (IRIT, IRIT-SIG, INUC), Andr\'e Peninou (IRIT, IRIT-SIG, UT2J), Olivier Teste (IRIT-SIG, IRIT, UT2J, Comue de Toulouse), 3 Oct 2025, A Robust Clustered Federated Learning Approach for Non-IID Data with Quantity Skew, https://arxiv.org/abs/2510.03380
• Taha M. Mahmoud and Naima Kaabouch, 3 Oct 2025, A Lightweight Federated Learning Approach for Privacy-Preserving Botnet Detection in IoT, https://arxiv.org/abs/2510.03513
• Jiahao Zeng, Wolong Xing, Liangtao Shi, Xin Huang, Jialin Wang, Zhile Cao, Zhenkui Shi, 4 Oct 2025, Personalized federated prototype learning in mixed heterogeneous data scenarios, https://arxiv.org/abs/2510.03726
• Xinwen Zhang, Hongchang Gao, 4 Oct 2025, On Provable Benefits of Muon in Federated Learning, https://arxiv.org/abs/2510.03866
• Aayushya Agarwal, Larry Pileggi, Gauri Joshi, 5 Oct 2025, Adaptive Federated Learning via Dynamical System Model, https://arxiv.org/abs/2510.04203
• Ziyi Chen, Su Zhang, Heng Huang, 6 Oct 2025, Trade-off in Estimating the Number of Byzantine Clients in Federated Learning, https://arxiv.org/abs/2510.04432
• Usman Akram, Yiyue Chen, Haris Vikalo, 6 Oct 2025, Federated Self-Supervised Learning for Automatic Modulation Classification under Non-IID and Class-Imbalanced Data, https://arxiv.org/abs/2510.04927
• Zainab Saad, Jialin Yang, Henry Leung, Steve Drew, 4 Oct 2025, Towards Carbon-Aware Container Orchestration: Predicting Workload Energy Consumption with Federated Learning, https://arxiv.org/abs/2510.03970
• Max Kirchner, Hanna Hoffmann, Alexander C. Jenke, Oliver L. Saldanha, Kevin Pfeiffer, Weam Kanjo, Julia Alekseenko, Claas de Boer, Santhi Raj Kolamuri, Lorenzo Mazza, Nicolas Padoy, Sophia Bano, Annika Reinke, Lena Maier-Hein, Danail Stoyanov, Jakob N. Kather, Fiona R. Kolbinger, Sebastian Bodenstedt, and Stefanie Speidel, 6 Oct 2025, Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge, https://arxiv.org/abs/2510.04772
• Jiaqi Wang, Xi Li, 5 Oct 2025, Position Paper: Assessing Robustness, Privacy, and Fairness in Federated Learning Integrated with Foundation Models, https://arxiv.org/abs/2402.01857
• Giuseppe Serra, Florian Buettner, 6 Oct 2025, Federated Continual Learning Goes Online: Uncertainty-Aware Memory Management for Vision Tasks and Beyond, https://arxiv.org/abs/2405.18925
• Seohyun Lee, Wenzhi Fang, Anindya Bijoy Das, Seyyedali Hosseinalipour, David J. Love, Christopher G. Brinton, 6 Oct 2025, Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning, https://arxiv.org/abs/2501.09320
• Bicheng Ying, Zhe Li, Haibo Yang, 5 Oct 2025, Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable, https://arxiv.org/abs/2503.20117
• Tinnakit Udsa, Can Udomcharoenchaikit, Patomporn Payoungkhamdee, Sarana Nutanong, Norrathep Rattanavipanon, 9 Oct 2025, Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning, https://arxiv.org/abs/2510.08750
• Jionghao Lou, Jian Zhang, Zhongmei Li, Lanlan Chen and Enbo Feng, 10 Oct 2025, FedL2T: Personalized Federated Learning with Two-Teacher Distillation for Seizure Prediction, https://arxiv.org/abs/2510.08984
• Dipam Goswami, Simone Magistri, Kai Wang, Bart{\l}omiej Twardowski, Andrew D. Bagdanov, Joost van de Weijer, 10 Oct 2025, Covariances for Free: Exploiting Mean Distributions for Training-free Federated Learning, https://arxiv.org/abs/2412.14326
• Murtaza Rangwala, Farag Azzedin, Richard O. Sinnott, Rajkumar Buyya, 10 Oct 2025, SketchGuard: Scaling Byzantine-Robust Decentralized Federated Learning via Sketch-Based Screening, https://arxiv.org/abs/2510.07922
• Sizhe Rao, Runqiu Zhang, Sajal Saha, and Liang Chen, 23 Oct 2025, An Ensembled Penalized Federated Learning Framework for Falling People Detection, https://arxiv.org/abs/2510.20960
• Jiaqi Xue, Mayank Kumar, Yuzhang Shang, Shangqian Gao, Rui Ning, Mengxin Zheng, Xiaoqian Jiang, Qian Lou, 24 Oct 2025, DictPFL: Efficient and Private Federated Learning on Encrypted Gradients, https://arxiv.org/abs/2510.21086
• Dandan Liang, Jianing Zhang, Evan Chen, Zhe Li, Rui Li, Haibo Yang, 24 Oct 2025, Towards Straggler-Resilient Split Federated Learning: An Unbalanced Update Approach, https://arxiv.org/abs/2510.21155
• Sana Ayromlou and Fatemeh Tavakoli and D. B. Emerson, 23 Oct 2025, Adaptive Latent-Space Constraints in Personalized Federated Learning, https://arxiv.org/abs/2505.07525
• Guiqiu Liao, Matjaz Jogan, Eric Eaton and Daniel A. Hashimoto, 24 Oct 2025, FORLA: Federated Object-centric Representation Learning with Slot Attention, https://arxiv.org/abs/2506.02964
• Tejash Varsani, 9 Oct 2025, Evaluation of Differential Privacy Mechanisms on Federated Learning, https://arxiv.org/abs/2510.09691
• Kahou Tam, Chunlin Tian, Li Li, Haikai Zhao and ChengZhong Xu, 13 Oct 2025, FedHybrid: Breaking the Memory Wall of Federated Learning via Hybrid Tensor Management, https://arxiv.org/abs/2510.11400
• Shouxu Lin, Zimeng Pan, Yuhang Yao, Haeyoung Noh, Pei Zhang, Carlee Joe-Wong, 12 Oct 2025, FLAMMABLE: A Multi-Model Federated Learning Framework with Multi-Model Engagement and Adaptive Batch Sizes, https://arxiv.org/abs/2510.10380
• Haifeng Wen, Hong Xing, Osvaldo Simeone, 11 Oct 2025, Pre-Training and Personalized Fine-Tuning via Over-the-Air Federated Meta-Learning: Convergence-Generalization Trade-Offs, https://arxiv.org/abs/2406.11569
• Xiaohong Yang, Minghui Liwang, Liqun Fu, Yuhan Su, Seyyedali Hosseinalipour, Xianbin Wang, Yiguang Hong, 13 Oct 2025, Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT, https://arxiv.org/abs/2503.06145
• Ting Wei, Biao Mei, Junliang Lyu, Renquan Zhang, Feng Zhou, Yifan Sun, 13 Oct 2025, Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation, https://arxiv.org/abs/2505.14161
• Santhosh Parampottupadam, Melih Co\c{s}\u{g}un, Sarthak Pati, Maximilian Zenk, Saikat Roy, Dimitrios Bounias, Benjamin Hamm, Sinem Sav, Ralf Floca, Klaus Maier-Hein, 11 Oct 2025, Inclusive, Differentially Private Federated Learning for Clinical Data, https://arxiv.org/abs/2505.22108
• Alessandro Licciardi, Davide Leo, Davide Carbone, 10 Oct 2025, Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning, https://arxiv.org/abs/2506.09674
• Zhanting Zhou and KaHou Tam and Zeqin Wu and Pengzhao Sun and Jinbo Wang and Fengli Zhang, 13 Oct 2025, FedIA: A Plug-and-Play Importance-Aware Gradient Pruning Aggregation Method for Domain-Robust Federated Graph Learning on Node Classification, https://arxiv.org/abs/2509.18171
• Drashthi Doshi, Aditya Vema Reddy Kesari, Avishek Ghosh, Swaprava Nath, Suhas S Kowshik, 13 Oct 2025, Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners, https://arxiv.org/abs/2505.12010
• Ankur Naskar, Gugan Thoppe, Utsav Negi, and Vijay Gupta, 8 Oct 2025, Parameter-Free Federated TD Learning with Markov Noise in Heterogeneous Environments, https://arxiv.org/abs/2510.07436
• Yunbo Li, Jiaping Gui, Zhihang Deng, Fanchao Meng, Yue Wu, 9 Oct 2025, FedQS: Optimizing Gradient and Model Aggregation for Semi-Asynchronous Federated Learning, https://arxiv.org/abs/2510.07664
• Linping Qu, Shenghui Song, Chi-Ying Tsui, 9 Oct 2025, FedLAM: Low-latency Wireless Federated Learning via Layer-wise Adaptive Modulation, https://arxiv.org/abs/2510.07766
• Huitong Jin, Yipeng Zhou, Quan Z. Sheng, Shiting Wen, Laizhong Cui, 9 Oct 2025, Mitigating Noise Detriment in Differentially Private Federated Learning with Model Pre-training, https://arxiv.org/abs/2408.09478
• Jiashi Gao, Ziwei Wang, Xiangyu Zhao, Xinming Shi, Xin Yao, Xuetao Wei, 9 Oct 2025, PFAttack: Stealthy Attack Bypassing Group Fairness in Federated Learning, https://arxiv.org/abs/2410.06509
• Rui Sun, Zhipeng Wang, Hengrui Zhang, Ming Jiang, Yizhe Wen, Jiahao Sun, Erwu Liu, Kezhi Li, 9 Oct 2025, Multi-Continental Healthcare Modelling Using Blockchain-Enabled Federated Learning, https://arxiv.org/abs/2410.17933
• Thanh Linh Nguyen and Quoc-Viet Pham, 10 Sep 2025, A Coopetitive-Compatible Data Generation Framework for Cross-silo Federated Learning, https://arxiv.org/abs/2509.18120
• Bishal K C, Amr Hilal, Pawan Thapa, 11 Sep 2025, Anomaly Detection in Electric Vehicle Charging Stations Using Federated Learning, https://arxiv.org/abs/2509.18126
• Zhaoxin Wang, Handing Wang, Cong Tian, Yaochu Jin, 23 Sep 2025, Enhancing the Effectiveness and Durability of Backdoor Attacks in Federated Learning through Maximizing Task Distinction, https://arxiv.org/abs/2509.18904
• Ferdinand Kahenga, Antoine Bagula, Sajal K. Das, Patrick Sello, 23 Sep 2025, FedFiTS: Fitness-Selected, Slotted Client Scheduling for Trustworthy Federated Learning in Healthcare AI, https://arxiv.org/abs/2509.19120
• Ferdinand Kahenga, Antoine Bagula, Patrick Sello, and Sajal K. Das, 23 Sep 2025, FedFusion: Federated Learning with Diversity- and Cluster-Aware Encoders for Robust Adaptation under Label Scarcity, https://arxiv.org/abs/2509.19220
• Kuai Yu, Xiaoyu Wu, Peishen Yan, Qingqian Yang, Linshan Jiang, Hao Wang, Yang Hua, Tao Song, Haibing Guan, 21 Oct 2025, POLAR: Policy-based Layerwise Reinforcement Learning Method for Stealthy Backdoor Attacks in Federated Learning, https://arxiv.org/abs/2510.19056
• Abdelkrim Alahyane (LAAS-SARA, LAAS), C\'eline Comte (CNRS, LAAS-SARA, LAAS), Matthieu Jonckheere (CNRS, LAAS-SARA, LAAS), \'Eric Moulines (X), 22 Oct 2025, Optimizing Asynchronous Federated Learning: A Delicate Trade-Off Between Model-Parameter Staleness and Update Frequency, https://arxiv.org/abs/2502.08206
• Alessio Masano, Matteo Pennisi, Federica Proietto Salanitri, Concetto Spampinato, Giovanni Bellitto, 30 Sep 2025, Zero-Shot Decentralized Federated Learning, https://arxiv.org/abs/2509.26462
• Kasun Eranda Wijethilake, Adnan Mahmood, Quan Z. Sheng, 25 Sep 2025, FedCLF - Towards Efficient Participant Selection for Federated Learning in Heterogeneous IoV Networks, https://arxiv.org/abs/2509.25233
• Yiwei Li, Shuai Wang, Zhuojun Tian, Xiuhua Wang and Shijian Su, 30 Sep 2025, Federated Learning with Enhanced Privacy via Model Splitting and Random Client Participation, https://arxiv.org/abs/2509.25906
• Xiao Zhang, Zengzhe Chen, Yuan Yuan, Yifei Zou, Fuzhen Zhuang, Wenyu Jiao, Yuke Wang, Dongxiao Yu, 30 Sep 2025, Data-Free Continual Learning of Server Models in Model-Heterogeneous Federated learning, https://arxiv.org/abs/2509.25977
• Yuki Takezawa, Anastasia Koloskova, Xiaowen Jiang, Sebastian U. Stich, 30 Sep 2025, FedMuon: Federated Learning with Bias-corrected LMO-based Optimization, https://arxiv.org/abs/2509.26337
• Seohyun Lee, Wenzhi Fang, Dong-Jun Han, Seyyedali Hosseinalipour, Christopher G. Brinton, 30 Sep 2025, TAP: Two-Stage Adaptive Personalization of Multi-task and Multi-Modal Foundation Models in Federated Learning, https://arxiv.org/abs/2509.26524
• Zhiyuan Ning, Chunlin Tian, Meng Xiao, Wei Fan, Pengyang Wang, Li Li, Pengfei Wang, Yuanchun Zhou, 30 Sep 2025, FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization, https://arxiv.org/abs/2405.06312
• Nour Jamoussi, Giuseppe Serra, Photios A. Stavrou and Marios Kountouris, 30 Sep 2025, Information-Geometric Barycenters for Bayesian Federated Learning, https://arxiv.org/abs/2412.11646
• Wei Zhuo, Zhaohuan Zhan, Han Yu, 30 Sep 2025, Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections, https://arxiv.org/abs/2505.23864
• Wanli Ni, Hui Tian, Shuai Wang, Chengyang Li, Lei Sun, Zhaohui Yang, 7 Oct 2025, Federated Split Learning for Resource-Constrained Robots in Industrial IoT: Framework Comparison, Optimization Strategies, and Future Directions, https://arxiv.org/abs/2510.05713
• Fan Liu, Bikang Pan, Zhongyi Wang, Xi Yao, Xiaoying Tang, Jingya Wang, Ye Shi, 7 Oct 2025, FLEx: Personalized Federated Learning for Mixture-of-Experts LLMs via Expert Grafting, https://arxiv.org/abs/2506.00965
• Ahmed Elhussein and Gamze Gursoy, 6 Oct 2025, A Universal Metric of Dataset Similarity for Cross-silo Federated Learning, https://arxiv.org/abs/2404.18773
• Haoyuan Li, Mathias Funk, Aaqib Saeed, 16 Oct 2025, Helmsman: Autonomous Synthesis of Federated Learning Systems via Multi-Agent Collaboration, https://arxiv.org/abs/2510.14512
• Maulidi Adi Prasetia, Muhamad Risqi U. Saputra, Guntur Dharma Putra, 16 Oct 2025, FedPPA: Progressive Parameter Alignment for Personalized Federated Learning, https://arxiv.org/abs/2510.14698
• Daniele Malpetti and Marco Scutari and Francesco Gualdi and Jessica van Setten and Sander van der Laan and Saskia Haitjema and Aaron Mark Lee and Isabelle Hering and Francesca Mangili, 16 Oct 2025, Technical and legal aspects of federated learning in bioinformatics: applications, challenges and opportunities, https://arxiv.org/abs/2503.09649

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