Decentralized federated learning through proxy model sharing

Shivam Kalra, Junfeng Wen, Jesse C. Cresswell, Maksims Volkovs () and H. R. Tizhoosh ()
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Shivam Kalra: Layer 6 AI
Junfeng Wen: Carleton University, School of Computer Science
Jesse C. Cresswell: Layer 6 AI
Maksims Volkovs: Layer 6 AI
H. R. Tizhoosh: University of Waterloo

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-38569-4

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