FDFL: Fair and discrepancy-aware incentive mechanism for federated learning

• Main Authors: CHEN, Zhe, ZHANG, Haiyan, LI, Xinghua, MIAO, Yinbin, ZHANG, Xiaohan, ZHANG, Man, MA, Siqi, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Federated learning; incentive mechanism; contribution evaluation; trusted execution environment; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/9638
• Institution: Singapore Management University

Federated Learning (FL) is an emerging distributed machine learning paradigm crucial for ensuring privacy-preserving learning. In FL, a fair incentive mechanism is indispensable for inspiring more clients to participate in FL training. Nevertheless, achieving a fair incentive mechanism in FL is an a...