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Novel clustered federated learning based on local loss

arXiv 2407.09360 ↗First posted 12 Jul 2024cs.LG math.OCcited by 0

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Abstract (arXiv)

This paper proposes LCFL, a novel clustering metric for evaluating clients' data distributions in federated learning. LCFL aligns with federated learning requirements, accurately assessing client-to-client variations in data distribution. It offers advantages over existing clustered federated learning methods, addressing privacy concerns, improving applicability to non-convex models, and providing more accurate classification results. LCFL does not require prior knowledge of clients' data distributions. We provide a rigorous mathematical analysis, demonstrating the correctness and feasibility of our framework. Numerical experiments with neural network instances highlight the superior performance of LCFL over baselines on several clustered federated learning benchmarks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 61 references that are in the map. First posted on arXiv 12 Jul 2024.

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References · 26

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14 more
  1. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  2. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
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  14. 2022Clustered Federated Learning based on Nonconvex Pairwise Fusioncited by 4

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