{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["14(1)"],"submitter":["Kalra S"],"pubmed_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 learn"],"journal":["Nature communications"],"pagination":["2899"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10203322"],"repository":["biostudies-literature"],"pubmed_title":["Decentralized federated learning through proxy model sharing."],"pmcid":["PMC10203322"],"pubmed_authors":["Tizhoosh HR","Cresswell JC","Wen J","Kalra S","Volkovs M"],"additional_accession":[]},"is_claimable":false,"name":"Decentralized federated learning through proxy model sharing.","description":"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 learn","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 May","modification":"2025-04-06T19:15:16.883Z","creation":"2025-04-06T19:15:16.883Z"},"accession":"S-EPMC10203322","cross_references":{"pubmed":["37217476"],"doi":["10.1038/s41467-023-38569-4"]}}