Decentralized Federated Learning through Proxy Model Sharing

• URL: http://arxiv.org/abs/2111.11343v2
• Date: Tue, 23 May 2023 02:18:27 GMT
• Title: Decentralized Federated Learning through Proxy Model Sharing
• Authors: Shivam Kalra, Junfeng Wen, Jesse C. Cresswell, Maksims Volkovs, Hamid R. Tizhoosh
• Abstract summary: We propose a communication-efficient scheme for decentralized federated learning called ProxyFL. We show that ProxyFL can outperform existing alternatives with much less communication overhead and stronger privacy.
• Score: 15.749416770494708
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing. Federated learning is a distributed learning framework that enables multi-institutional collaborations on decentralized data with improved protection for each collaborator's data privacy. In this paper, 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. Proxy models allow efficient information exchange among participants without the need of a centralized server. The proposed method eliminates a significant limitation of canonical federated learning by allowing model heterogeneity; each participant can have a private model with any architecture. Furthermore, our protocol for communication by proxy leads to stronger privacy guarantees using differential privacy analysis. Experiments on popular image datasets, and a cancer diagnostic problem using high-quality gigapixel histology whole slide images, show that ProxyFL can outperform existing alternatives with much less communication overhead and stronger privacy.

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