DEFEAT: A decentralized federated learning against gradient attacks
3
3
Shandong University
100128
Department of Computer Science, Georgia State University, Atlanta, 30303, United States; Department of Computer Science, Bowling Green State University, Bowling Green, 43403, United States; Computer Information Systems Department, Al Quds Open University, Ramallah, 90917, Palestine
Lu G., Department of Computer Science, Georgia State University, Atlanta, 30303, United States; Xiong Z., Department of Computer Science, Georgia State University, Atlanta, 30303, United States; Li R., Department of Computer Science, Bowling Green State University, Bowling Green, 43403, United States; Mohammad N., Computer Information Systems Department, Al Quds Open University, Ramallah, 90917, Palestine; Li Y., Department of Computer Science, Georgia State University, Atlanta, 30303, United States; Li W., Department of Computer Science, Georgia State University, Atlanta, 30303, United States
W. Li; Department of Computer Science, Georgia State University, Atlanta, 30303, United States; email: wli28@gsu.edu
19
10.1016/j.hcc.2023.100128
Federated learning; Peer to peer network; Privacy protection
Distributed computer systems; Machine learning; Centralised; Decentralised; Federated learning; Global models; Learning frameworks; Local model; Machine-learning; Peer-to-peer networks; Privacy protection; Private data; Peer to peer networks
