Federated Learning

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91 papers in the last four weeks, up 176% on the four weeks before. 0.9% of all new papers.

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Federated Learning over Human-Body Communication for On-Body Edge Intelligence: A Survey, Taxonomy, and BODYFED-HBC Scheduling Vignette
May 22, 2026Koffka KhanClient Selection in Federated Learning Federated Learning
Department of Computing and Information Technology The University of the West Indies, St. Augustine, Trinidad and Tobago
FederatedRSF : Federated Random Survival Forests for Partially Overlapping Medical Data
May 21, 2026Maryam Moradpour, Jonas Harriehausen, Amirreza Aleyasin +3Federated Learning Aggregation Cancer Survival Prediction
Institute for Predictive Deep Learning in Medicine and Healthcare, Justus Liebig University Gießen, Germany · Hessian Center for Artificial Intelligence (hessian.AI), Darmstadt, Germany · Department of Medical Informatics, University Medical Center Göttingen, Germany +1
Building a privacy-preserving Federated Recommender system for mobile devices
May 21, 2026Aasheesh SinghPrivacy-Preserving ML Recommender Systems
Université de Montréal · Département d’informatique et de recherche opérationnelle · Faculté des arts et des sciences
FIRMA: FIbonacci Ring Model Aggregation for Privacy-preserving Federated Learning
May 21, 2026Rachid HedjamFederated Learning Aggregation Non-IID Federated Learning
Department of Computer Science Bishop’s University Sherbrooke, Qc, Canada
EnCAgg: Enhanced Clustering Aggregation for Robust Federated Learning against Dynamic Model Poisoning
May 21, 2026Tianyun Zhang, Zhen Yang, Haozhao Wang +2Federated Learning Aggregation Byzantine-Robust Federated Learning
School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China · School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China · Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Detecting Atypical Clients in Federated Learning via Representation-Level Divergence
May 21, 2026Cristian Pérez-Corral, Jose I. Mestre, Alberto Fernández-Hernández +2Federated Learning Aggregation Non-IID Federated Learning
Universitat Politècnica de València Valencia, Spain · Universitat Jaume I Castelló de la Plana, Spain
Embedding-Based Federated Learning with Runtime Governance for Iron Deficiency Prediction
May 20, 2026Fan Zhang, Simon Deltadahl, Majid Lotfian Delouee +10Federated Learning Aggregation Healthcare
Dept. of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge, UK · Translational AI Laboratory, Dept. of Laboratory Medicine Amsterdam UMC Amsterdam, The Netherlands · Precision Health University Research Institute Queen Mary Univ. of London London, UK +3
Optimized Federated Knowledge Distillation with Distributed Neural Architecture Search
May 20, 2026Chaimaa Medjadji, Sylvain Kubler, Yves Le Traon +3Communication-Efficient Distributed Training Federated Knowledge Distillation
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg · Blekinge Institute of Technology, Sweden · ADSLabs, Umea University, Sweden
CRAFT: Conflict-Resolved Aggregation for Federated Training
May 20, 2026Ziqi Wang, Qiang Liu, Nils ThuereyFederated Learning Aggregation Gradient Interference
Department of Mathematics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany. · School of Computation, Information and Technology, Technical University of Munich, Garching, Germany. · Munich Center for Machine Learning, Munich, Germany.
FedCoE: Bridging Generalization and Personalization via Federated Coordinated Dual-level MoEs
May 20, 2026Penglin Dai, Fulian Li, Xincao Xu +3Federated Learning Aggregation Non-IID Federated Learning
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China · Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China · School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Federated LoRA Fine-Tuning for LLMs via Collaborative Alignment
May 20, 2026Shuaida He, Liwen Chen, Long FengLLM Fine-Tuning Low-Rank Adaptation
School of Computing & Data Science, The University of Hong Kong
Automated Byzantine-Resilient Clustered Decentralized Federated Learning for Battery Intelligence in Connected EVs
May 20, 2026Mouhamed Amine Bouchiha, Abdelaziz Amara Korba, Yacine Ghamri-DoudaneByzantine-Robust Federated Learning Decentralized Learning
SAMOVAR, T´el´ecom SudParis, Paris, France · Department of Computer Science, German University of Technology in Oman (GUtech), Muscat, Oman · L3i, La Rochelle University, La Rochelle, France
A Typed Tensor Language for Shared-State Federated Computation
May 20, 2026Theofilos Mailis, Theodore Papamarkou, Andreas Ktenidis +6Federated Learning Aggregation Federated Learning
Athena Research Center · National and Kapodistrian University of Athens · National Technical University of Athens
Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning
May 20, 2026Adda Akram Bendoukha, Heber Hwang Arcolezi, Nesrine Kaaniche +1Client Selection in Federated Learning Non-IID Federated Learning
Samovar, T´el´ecom SudParis, Institut Polytechnique de Paris, France · ´ETS Montr´eal · CEA-LIST
Distributed Direct Preference Optimization
May 20, 2026Zhanhong JiangDirect Preference Optimization Non-IID Federated Learning
Translational AI Center, Iowa State University, Ames, USA.
Causal Unlearning in Collaborative Optimization: Exact and Approximate Influence Reversal under Adversarial Contributions
May 19, 2026Ali Mahdavi, Azadeh Zamanifar, Amirfarhad Farhadi +1Federated Unlearning Krylov Subspace Methods
Department of Computer Engineering SRC, Islamic Azad University Tehran, Iran · School of Computer Engineering Iran University of Science and Technology Tehran, Iran · Meta CA, USA
A Van Trees Lower Bound for Fully Interactive Differentially Private Federated Learning
May 19, 2026T. Tony Cai, Yicheng LiDifferentially Private Federated Learning Differential Privacy
Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA. · KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai, China.
Gaussian Approximation and Multiplier Bootstrap for Federated Linear Stochastic Approximation
May 19, 2026Ilya Levin, Maksim Shuklin, Eric Moulines +2Stochastic Approximation Federated Learning
1HSE University, Russia · 2MBZUAI · 3CMAP, CNRS, École Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France
Do Vision Models Truly Forget? New Findings from Representation-Level Certification of Visual Unlearning in Vertical Federated Learning
May 19, 2026Zhenyu Yu, Yangchen Zeng, Chunlei Meng +2Federated Unlearning Machine Unlearning
College of Computer Science and Artificial Intelligence, Fudan University · School of Cyber Science and Engineering, Southeast University · College of Intelligent Robotics and Advanced Manufacturing, Fudan University +1
OmniISR: A Unified Framework for Centralized and Federated Learning via Intermediate Supervision and Regularization
May 19, 2026Wei-Bin Kou, Guangxu Zhu, Ming Tang +4Federated Learning Heterogeneous Federated Learning
Yinwang Intelligent Technology Co. Ltd., Shenzhen, China
FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data
May 18, 2026Nuredin Ali Abdelkadir, Anjali Ratnam, Zeerak Talat +1Depression Detection Mental Health
University of Minnesota · University of Edinburgh
Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning
May 18, 2026Arnab Auddy, Xiangni Peng, Subhadeep PaulFederated Learning Aggregation Communication-Efficient Distributed Training
Department of Statistics · The Ohio State University
Federated Martingale Posterior Samping
May 18, 2026Boning Zhang, Matteo Zecchin, Mingzhao Guo +2Bayesian Neural Networks Posterior Sampling
School of Computing, University of Glasgow, Glasgow G12 8QQ, U.K. · Communication Systems Department, EURECOM, 06904 Sophia Antipolis, France · Institute for Intelligent Networked Systems, Northeastern University London, London E1 8PH, U.K.
Quantum Machine Learning-based 6G edge Network: Enabling Adaptive Communication and Model Aggregation
May 18, 2026Wenjing Xiao, Jiatai Yan, Chenglong Shi +5Semantic Communication Quantum Machine Learning
School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China, and also with the Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi 530004, China · School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China, and also with the Pazhou Laboratory, Guangzhou 510330, China · Qatar Center for Quantum Computing, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar +1
Can Quantum Federated Learning Withstand Circuit-Level Backdoors?
May 18, 2026Aakar Mathur, Mohammed Ruknuddin, Ashish GuptaBackdoor Attacks in Federated Learning Quantum Machine Learning
BITS Pilani Dubai Campus, Dubai, UAE
FedSDR: Federated Self-Distillation with Rectification
May 18, 2026Ziheng Ren, Zhanming Shen, Hao Wang +2LLM Fine-Tuning Hallucination in Language Models
Beijing University of Aeronautics and Astronautics · Zhejiang University · Stevens Institute of Technology +1
Federated Learning by Utility-Constrained Stochastic Aggregation for Improving Rational Participation
May 18, 2026M Yashwanth, Arunabh Singh, Ashok Nayak +2Federated Learning Aggregation Federated Learning
Indian Institute of Science, Bangalore · Indian Institute of Technology Bombay · IIIT Hyderabad
Q-LocalAdam: Memory-Efficient Client-Side Adaptive Optimization for Edge Federated Learning
May 17, 2026Vedant Waykole, Haroon R. LoneNon-IID Federated Learning Edge ML
IISER Bhopal · India
BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregation
May 17, 2026Yuhan Xie, Chen Lyu, Jingrong HuangFederated Learning Aggregation Split Federated Learning
Shanghai University of Finance and Economics, China · MoE Key Laboratory of Interdisciplinary Research of Computation and Economics, China.
TSFLora: Token-Compressed Split Fine-Tuning for Wireless Edge Networks
May 17, 2026Xianke Qiang, Zheng Chang, Li Wang +1Communication-Efficient Distributed Training Edge Computing
School of Computer Science and Engineering, UESTC, 611731 Chengdu, China · School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China. · Center for Intelligent Networking and Communications, UESTC, 611731 Chengdu, China.

May 22, 2026, cs.LGJ/K move · Enter open · S save

Federated Learning over Human-Body Communication for On-Body Edge Intelligence: A Survey, Taxonomy, and BODYFED-HBC Scheduling Vignette

Koffka Khan

Department of Computing and Information Technology The University of the West Indies, St. Augustine, Trinidad and Tobago

Client Selection in Federated Learning Federated Learning

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Human-body communication (HBC) is a promising physical substrate for wearable body-area networks because it can localize communication around the body and reduce the burden of conventional radio links. Federated learning (FL) is a promising learning substrate because it can reduce raw-data centralization for physiological and behavioral sensing. Yet these two literatures remain weakly connected: FL for wearables usually abstracts the communication layer, whereas HBC research usually abstracts learning and model-update traffic. This article surveys the intersection of HBC, wireless body-area networks, wearable FL, Internet-of-Bodies privacy, and edge-intelligence optimization. We propose a taxonomy that distinguishes intra-body, body-hub, cross-user, and clinical-cloud FL deployments, and we identify the open problem of body-channel-aware FL: learning protocols whose client selection, update compression, and aggregation are controlled by posture-dependent HBC links, residual energy, sensor memory, and privacy risk. To make the research agenda concrete, we introduce BODYFED-HBC as a reference architecture and provide an optimization formulation and scheduling algorithm. We further specify a reproducible simulation vignette that combines public wearable datasets with empirical body-coupled-communication signal-loss models. The article concludes with open datasets, evaluation metrics, limitations, and research directions for computer scientists working above the hardware layer.

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