mlsanityData to 7 Oct 2026

FilFL: Client Filtering for Optimized Client Participation in Federated Learning

arXiv 2302.06599 ↗First posted 13 Feb 2023cs.LG cs.AI cs.DCcited by 5

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Abstract (arXiv)

Federated learning, an emerging machine learning paradigm, enables clients to collaboratively train a model without exchanging local data. Clients participating in the training process significantly impact the convergence rate, learning efficiency, and model generalization. We propose a novel approach, client filtering, to improve model generalization and optimize client participation and training. The proposed method periodically filters available clients to identify a subset that maximizes a combinatorial objective function with an efficient greedy filtering algorithm. Thus, the clients are assessed as a combination rather than individually. We theoretically analyze the convergence of federated learning with client filtering in heterogeneous settings and evaluate its performance across diverse vision and language tasks, including realistic scenarios with time-varying client availability. Our empirical results demonstrate several benefits of our approach, including improved learning efficiency, faster convergence, and up to 10% higher test accuracy than training without client filtering.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 45 of its 65 references that are in the map. First posted on arXiv 13 Feb 2023.

Builds on

45 of 65 references.

References · 45

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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33 more
  1. 2018LEAF: A Benchmark for Federated Settingscited by 780
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  28. 2022AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimationcited by 21
  29. 2023Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedbackcited by 19
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 0; 2026: 1.

5 papers.

  1. 2024Federated Combinatorial Multi-Agent Multi-Armed Banditscited by 11
  2. 2024Flashback: Understanding and Mitigating Forgetting in Federated Learningcited by 4
  3. 2024Decentralized Personalized Federated Learningcited by 1
  4. 2024Where is the Testbed for my Federated Learning Research?cited by 0
  5. 2026Multi-Agent Combinatorial-Multi-Armed-Bandit framework for the Submodular Welfare Problem under Bandit Feedbackcited by 0