biostudies-literatureUnknown14(1)Kalra SInstitutions 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 learnNature communications2899https://www.ebi.ac.uk/biostudies/studies/S-EPMC10203322biostudies-literatureDecentralized federated learning through proxy model sharing.PMC10203322Tizhoosh HRCresswell JCWen JKalra SVolkovs MfalseDecentralized federated learning through proxy model sharing.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 learn2023-01-01T00:00:00Z2023 May2025-04-06T19:15:16.883Z2025-04-06T19:15:16.883ZS-EPMC102033223721747610.1038/s41467-023-38569-4