Author

Zhenglun Kong

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FedLDCS: Adaptive Divergence-Based Client Selection for Federated Learning

This paper proposes a novel Largest Distance Client Selection (LDCS) method that prioritizes clients based on the divergence of their local models from the global model, as quantified by the Frobenius norm, thereby improving training efficiency and model performance while overcoming the limitations of existing random or loss-based approaches.

Shiyue Hou, Zhenglun Kong · 2 citations