Novel clustered federated learning based on local loss

• URL: http://arxiv.org/abs/2407.09360v1
• Date: Fri, 12 Jul 2024 15:37:05 GMT
• Title: Novel clustered federated learning based on local loss
• Authors: Endong Gu, Yongxin Chen, Hao Wen, Xingju Cai, Deren Han,
• Abstract summary: This paper proposes LCFL, a novel metric for evaluating data distributions in learning. It aligns with learning requirements, accurately addresses privacy concerns, and provides more accurate classification.
• Score: 14.380553970274242
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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