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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Client and Training Data Selection for Computationally Efficient Synchronized Federated Learning
Sep 30, 2026Muzaffer Citir, Hiroki Nishikawa, Sangyoung ParkClient Selection in Federated Learning Non-IID Federated Learning
Smart Mobility Systems, Technical University of Berlin, Germany · Graduate School of Information Science and Technology, The University of Osaka, Japan
HO-FL: Hybrid-Order Federated Learning for Heterogeneous Edge Devices
Sep 30, 2026Qiyuan Chen, Xian Wu, Yanan Ma +1On-Device Training Federated Learning
University of Hong Kong · City University of Hong Kong
Aegis: Generative Gradient Masking for Privacy-Preserving Medical Federated Learning
Sep 29, 2026Chaoyu Zhang, Shanghao Shi, Heng Jin +3Model Inversion Attacks Federated Learning
Virginia Tech · Washington University in St. Louis · University of South Florida
FedSocket: Recipient-Executable Knowledge Exchange for Heterogeneous Multimodal Federated Learning
Sep 29, 2026Xinyuan ZhaoCross-Modal Knowledge Distillation Federated Knowledge Distillation
Sun Yat-sen University
FedLAFP: Low-Rank Aggregation Meets Full-Rank Personalization in Federated Fine-Tuning
Sep 29, 2026Mengjun Yi, Huaian Gu, Yinghao Ai +2Low-Rank Adaptation Federated Fine-Tuning
State Key Laboratory for Novel Software Technology · School of Artificial Intelligence · School of Computer Science +1
OPFL: Optimistic Verification of Federated Learning via Empirical Boundary
Sep 29, 2026Hongxu Su, Jianzhu Yao, Xuechao Wang +1Federated Learning
CF-LoRA: Decoupled Factor Aggregation and Adaptation-Aware Client Clustering for Federated LoRA Fine-Tuning
Sep 29, 2026Mengjun Yi, Langxing Yang, Suhan Guo +2Federated Learning Aggregation Federated Fine-Tuning
State Key Laboratory for Novel Software Technology · School of Artificial Intelligence School of Electronic Science and Engineering Nanjing University
DecoyTrace: Toxic Decoys for Active Defense in Decentralized Federated Learning
Sep 28, 2026Pedro Beltrán-López, Enrique Tomás Martínez Beltrán, Pantaleone Nespoli +2Byzantine-Robust Federated Learning Backdoor Attacks in Federated Learning
Department of Information and Communication Engineering, University of Murcia, Murcia, 30100, Spain
Encoder-Sharing Hierarchical Federated Multi-Task Learning for VANETs
Sep 28, 2026M. Saeid HaghighiFard, Sinem ColeriHierarchical Federated Learning Intelligent Transportation Systems
Department of Electrical and Electronics Engineering, Koc¸ University, Istanbul, Türkiye
Making Cross-Continental Federated Learning Repeatable with FLIP: a Multi-Application Study
Sep 28, 2026Rafael Garcia-Dias, Alexandre Triay Bagur, Chayanin Tangwiriyasakul +20Healthcare ML Reproducibility
School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK · Bangkok Dusit Medical Services, Bangkok, Thailand
CTP-FL: Common-Trajectory Gradient Prediction for Federated Learning
Sep 28, 2026Junkang LiuFederated Learning
Tianjin University
RoboFL: Federated Expert Assembly for World Action Models
Sep 28, 2026Rongyu Zhang, Ruizhi Fan, Yunfan Lou +8Mixture of Experts World Action Models
Nanjing University · Hong Kong University of Science and Technology · State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University
Physics-Attested Federated Learning: Securing Collaborative Anomaly Detection in Critical Water Infrastructure
Sep 28, 2026Jeff Nijsse, Shu Su, Benjamin Oholeguy +1Byzantine-Robust Federated Learning Industrial Anomaly Detection
Department of Software Engineering, RMIT University, Hanoi, Vietnam · Department of Mathematical Sciences, Auckland University of Technology, New Zealand · Independent Researcher, New Zealand +1
Agentic Federated Learning: Rule-Based Client and Server Agents for Adaptive Training
Sep 28, 2026Deepthy K. Bhaskar, VP Binu, B MinimolFederated Learning Aggregation Client Selection in Federated Learning
Department of Computer Engineering, Model Engineering College, APJ Abdul Kalam Technological University, Thiruvananthapuram 695016, Kerala, India. · Department of Biomedical Engineering, Model Engineering College, APJ Abdul Kalam Technological University, Thiruvananthapuram 695016, Kerala, India.
SPADE-DFL: Communication-Efficient Decentralized Federated Learning via Derivative-Free Linearized ADMM
Sep 24, 2026Mengli Wei, Mengkai Zhu, Jiawen Chen +2Communication-Efficient Distributed Training Black-Box Optimization
School of Mathematics, Southeast University, Nanjing 210096, China
Personalised federated learning for Riemannian and Euclidean EEG decoding
Sep 24, 2026Thibault Pautrel, Florent Bouchard, Ammar Mian +1EEG Decoding Federated Learning
Universit´e Paris-Saclay, CNRS, CentraleSup´elec, L2S, France · Universit´e Savoie Mont-Blanc, LISTIC, France
Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunities
Sep 23, 2026Pravija Raj P, Ashish Gupta, Andrea Augello +1Federated Learning Aggregation Adversarial Robustness
Department of Computer Science and Engineering, BITS Pilani Dubai Campus, Dubai, UAE · Department of Engineering, University of Palermo, Palermo, Italy · Department of Computer Science, Missouri University of Science and Technology, Rolla, USA
Federated Learning of AnDE Classifiers
Sep 23, 2026Pablo Torrijos, Juan C. Alfaro, José A. Gámez +1Federated Learning
Departamento de Sistemas Informáticos. Universidad de Castilla-La Mancha, 02071 Albacete, Spain. · Instituto de Investigación en Informática de Albacete. Universidad de Castilla-La Mancha, 02071 Albacete, Spain.
Fed-ReMasker: Federated Tabular Imputation under Feature-Level Missingness
Sep 23, 2026Ioannis Papathanail, Rooholla Poursoleymani, Lubnaa Abdur Rahman +1Incomplete Data Imputation Masked Autoencoders
ARTORG Center for Biomedical Engineering Research University of Bern, Bern, Switzerland · Graduate School for Cellular and Biomedical Sciences University of Bern, Bern, Switzerland
FedIncome: Federated Learning for Income Estimation in Digital Lending Under Data Sovereignty Constraints
Sep 23, 2026Sultan Amed, Tanmay Sen, Sayantan BanerjeeCredit Scoring Finance
OM & QT Area, Indian Institute of Management Indore, M.P. 453556, India. · SQC & OR Unit, Indian Statistical Institute Kolkata, W.B. 700108, India
A generalizable structural brain MRI foundation model built through dual-priority federated pretraining
Sep 23, 2026Zhen Yu, Yang Liu, Xiahai Zhuang +1Medical Imaging Foundation Models Federated Learning Aggregation
National Institute of Health Data Science, Peking University, Beijing, 100871, China. · Institute of Medical Technology, Peking University Health Science Center, Beijing, 100871, China. · State Key Laboratory of General Artificial Intelligence, Peking University, Beijing, 100871, China. +2
Reliable Federated TinyML Deployment for IoT Security
Sep 23, 2026Younsoo Park, Seokhyoen Bae, Shasi Kumar Ramachandran Prabhu +2IoT Intrusion Detection Tiny ML
The Pennsylvania State University, University Park, Pennsylvania, USA · Elizabethtown College, Elizabethtown, Pennsylvania, USA
On the Gradient Heterogeneity Dynamics of Adversarially Robust Federated Regression
Sep 22, 2026Leonardo F. Toso, James Anderson, Nirupam Gupta +1Byzantine-Robust Federated Learning Non-IID Federated Learning
Columbia University, USA · University of Copenhagen, Denmark · Sorbonne Université and Université Paris Cité, CNRS, LPSM, France
A Practical Recipe for Semi-Supervised Federated ASR: Online Pseudo-Labels with Server Update Stabilization
Sep 21, 2026Wonho Bae, Zakaria Aldeneh, Martin Pelikan +3Automatic Speech Recognition Pseudo-Labeling
Apple
A Federated Artificial Intelligence Framework for Optimizing Pancreatic Cancer Treatment - Strategy Update
Sep 21, 2026Anne-Christin Hauschild, Amirreza Aleyasin, Nils H. Beyer +12Clinical Decision Support Federated Learning
University Medical Center Göttingen, Department of Medical Informatics, Göttingen, Germany · University of Göttingen, Campus Institute Data Science (CIDAS), Section of Medical Data Science, Göttingen, Germany · Institute for Predictive Deep Learning for Medicine and Healthcare, Justus-Liebig University, Gießen, Germany +13
FedMust: Semi-supervised Multi-task Student-Teacher Federated Learning for Multi-organ CT Segmentation
Sep 21, 2026Ashkan Moradi, Bendik Skarre Abrahamsen, Mattijs ElschotMulti-Task Learning Semi-Supervised Medical Image Segmentation
Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway · Central Staff at St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
Adaptive Determinantal Client Scheduling in Federated Learning
Sep 20, 2026Wen Xu, Ben Liang, Gary Boudreau +1Client Selection in Federated Learning Federated Learning
University of Toronto, Canada · Ericsson, Canada
Federated Multilingual Speech-LLMs: Architecture and Aggregation Strategy Benchmarking
Sep 20, 2026Jordi Luque, Aleix Sant, Fernando LópezSpeech Language Models Federated Learning
Telefónica Innovación Digital, Scientific Research
SAGE: Optimal-Stopping Peer Selection for Decentralised Federated Learning
Sep 20, 2026Ke Xiao, Qiyuan Wang, Christos AnagnostopoulosOptimal Stopping Adaptive Sampling
School of Computing Science, University of Glasgow, Glasgow, UK
Feature Suppression and Differential Privacy for Residential Traffic Classification: A Two-Home Federated Study
Sep 20, 2026Márton Pál Lipcsey-Magyar, Adrian PekarDifferentially Private Stochastic Gradient Descent Federated Learning
Budapest University of Technology and Economics, Hungary · CUJO LLC Hungary

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Client and Training Data Selection for Computationally Efficient Synchronized Federated Learning

Muzaffer Citir, Hiroki Nishikawa, Sangyoung Park

Smart Mobility Systems, Technical University of Berlin, Germany · Graduate School of Information Science and Technology, The University of Osaka, Japan

Client Selection in Federated Learning Non-IID Federated Learning Training Data Selection Federated Learning Heterogeneous Federated Learning

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Federated learning (FL) is a promising paradigm of machine learning, which preserves user privacy by enabling learning without sharing raw data with a cloud server. Straggling clients have been a problem for FL as they introduce delays in aggregating the local models and hence, the convergence of the global model. Therefore, it is important to have a mechanism that ensures fast convergence of the global model as well as good FL participation rate. Another issue for the convergence of a model in FL is the non-independent and identically distributed (non-iid) data across the clients. Prior approaches based on probabilistic client selection do not work well under non-iid data especially when the number of clients is small. We show scenarios where such approaches fail and propose a joint client-training data selection algorithm for fast convergence of FL models. Our experiments on CIFAR-100 dataset show that convergence of the FL model can be significantly improved over prior works that can consider non-iid data and heterogeneous computation and higher model accuracy.

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