Online Federated Learning

Latest papers

9 papers3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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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Beyond Aggregate Drift: Optimal Client-Specific Windows in Federated Learning

Yuriy Dorn, Dmitrii Vishovan, Kirill Novoselov, Ilya Kuruzov+2

Lomonosov Moscow State University · Innopolis University · Moscow Independent Research Institute of Artificial Intelligence+3

Federated Learning Online Federated Learning Federated Continual Learning Distributed Optimization Non-IID Federated Learning

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We study federated learning from continuously arriving data: each client samples from its own distribution, these distributions drift over time, and the server must track the minimizer of the clients' weighted average loss. Clients are not alike as they collect data at different rates, with different noise, and change at different speeds. The main question is how much history should each one keep? A single shared window must serve every client at once, and so makes a fast-changing client carry the sampling noise of a slow, nearly stationary one. We give each client a memory of its own, sized to that client's noise, arrival rate, and drift, and refresh it by restarted accelerated method that starts from a common point. The tracking error is then bounded by

R\left(\sum_m w_m^{3/2}\,σ_m\sqrt{γ_m/λ_m}\right)^{2/3},

for clients with weight

w_m

, noise

σ_m

, arrival rate

λ_m

, and drift rate

γ_m

on a domain of diameter

R

. This is never worse than what any common window can achieve, and strictly better whenever noise and drift are spread unevenly across clients. A lower bound that puts different groups of clients on different drift clocks shows this quantity is the right one. The two bounds agree up to a constant whenever the clients occupy boundedly many refresh scales, and in general differ only by the sixth root of the number of scales. Experiments show the gain comes from the memory rather than from the solver: it persists across accelerated, stochastic-gradient, and one-step methods, and against a separately tuned common-memory baseline.