Aussie AI

Federated Learning

Last Updated 29 August, 2025

by David Spuler, Ph.D.

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