Online Federated Learning

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10 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

On the Generalization Gap of Streaming Federated Learning with Markovian Data
Oct 28, 2026Masoud Kavian, Milad Sefidgaran, Youssef Attia El Hili +2Federated Learning Online Federated Learning
Huawei Paris Research Center, Huawei Technologies Ltd., France · Huawei Noah’s Ark Lab, Paris, France · Université Gustave Eiffel, France
Beyond Aggregate Drift: Optimal Client-Specific Windows in Federated Learning
Oct 11, 2026Yuriy Dorn, Dmitrii Vishovan, Kirill Novoselov +3Federated Learning Online Federated Learning
Lomonosov Moscow State University · Innopolis University · Moscow Independent Research Institute of Artificial Intelligence +3
Communication-Efficient Agnostic Federated Learning via Faster Convergence and Compression
Sep 29, 2026Haomin Bai, Junyan Sun, Sifan Yang +2Error Feedback Online Federated Learning
State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, China · School of Artificial Intelligence, Nanjing University, Nanjing, China · Department of Computer Science, City University of Hong Kong, Hong Kong, China
Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis
Sep 14, 2026Bassel Soudan, Sohail Abbas, Ahmed Kubba +2Online Federated Learning Federated Learning
Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah, UAE · Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah, UAE
Robust Reputation-Driven Crowdsourced Federated Learning
Aug 9, 2026Mouhamed Amine Bouchiha, Gregory BlancByzantine-Robust Federated Learning Backdoor Attacks
SAMOVAR, Télécom SudParis, Institut Polytechnique de Paris
UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment
May 8, 2026Shih-Yu Lai, Hirozumi Yamaguchi, Shang-Tse Chen +2Multimodal Federated Learning Federated Learning Aggregation
National Taiwan University · RIKEN-CCS · The University of Osaka +1
Beyond Rigid Alignment: Graph Federated Learning via Dual Manifold Calibration
May 7, 2026Wentao Yu, Bo Han, Jie Yang +1Federated Graph Learning Manifold Learning
School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing, China · Department of Computer Science Hong Kong Baptist University Hong Kong, China · School of Automation and Intelligent Sensing Shanghai Jiao Tong University Shanghai, China
Graph Federated Unlearning for Privacy Preservation
May 4, 2026Ruotong Ma, Wentao Yu, Qizhou Wang +2Federated Unlearning Federated Graph Learning
Shanghai Jiao Tong University · Nanjing University of Science and Technology · Hong Kong Baptist University China
FedSEA: Achieving Benefit of Parallelization in Federated Online Learning
Apr 21, 2026Harekrushna Sahu, Pratik Jawanpuria, Pranay SharmaOnline Convex Optimization Non-IID Federated Learning
CMInDS, IIT Bombay, India
FedACT: Concurrent Federated Intelligence across Heterogeneous Data Sources
Mar 11, 2026Md Sirajul Islam, Isabelle G Chapman, N I Md Ashafuddula +4Online Federated Learning Federated Learning
School of Computing and Informatics, University of Louisiana at Lafayette, USA · Department of Computer and Information Sciences, University of Delaware, USA · Department of Computer Science, University of Illinois at Urbana-Champaign, USA

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On the Generalization Gap of Streaming Federated Learning with Markovian Data

Masoud Kavian, Milad Sefidgaran, Youssef Attia El Hili, Malik Tiomoko+1

Huawei Paris Research Center, Huawei Technologies Ltd., France · Huawei Noah’s Ark Lab, Paris, France · Université Gustave Eiffel, France

Federated Learning Online Federated Learning

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Streaming federated learning (SFL) allows clients to train a shared model from continuously arriving data without retaining large local datasets. Such streams often exhibit temporal dependence, yet its effect on generalization remains poorly understood. In this paper we study the expected generalization gap of the aggregated model when client data follow Markov processes, using information-theoretic and rate-distortion techniques. Our framework accommodates heterogeneous Markovian dynamics across clients and distinguishes temporal dependence from information acquired during local learning. We establish bounds under cross-entropy loss, specializing them to Linear Next-Token Prediction (LNTP) and Self-Attention Next-Token Prediction (SANTP). These results quantify the effects of client count, communication rounds, sample size, data dimension, mixing time, and context length. In particular, they reveal a communication--generalization tradeoff: although frequent aggregation may help optimization, it can weaken generalization guarantees. They also suggest a potential advantage of distributed over centralized streaming learning under a fixed total data budget. Temporal dependence enters through the square root of the mixing time. By analyzing the history-state process, we obtain refined bounds with improved context-length dependence under suitable mixing conditions. Experiments on GPT-style language-model pretraining with TinyStories and real-world time-series forecasting illustrate how collaboration and communication frequency influence generalization under temporal dependence