Decentralized Federated Learning through Proxy Model Sharing
Published in Nature Communications, 2023
Recommended citation: Shivam Kalra, Junfeng Wen, Jesse C. Cresswell, Maksims Volkovs, and Hamid R. Tizhoosh. Decentralized federated learning through proxy model sharing. Nature Communications 14, 2899, 2023.
We propose a communication-efficient scheme for decentralized federated learning called ProxyFL, or proxy-based federated learning. Each participant in ProxyFL maintains two models, a private model, and a publicly shared proxy model designed to protect the participant’s privacy.
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