An Efficiency-Boosting Client Selection Scheme for Federated Learning With Fairness Guarantee

• Published in: IEEE transactions on parallel and distributed systems Vol. 32; no. 7; pp. 1552 - 1564
• Main Authors: Huang, Tiansheng, Lin, Weiwei, Wu, Wentai, He, Ligang, Li, Keqin, Zomaya, Albert Y.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Client selection; Clients; Collaboration; Computer networks; Computer science; contextual combinatorial multi-arm bandit; Data models; Distributed processing; fairness scheduling; Federated learning; lyapunov optimization; Mathematical models; Optimization; Privacy; Problem solving; Servers; Training

The issue of potential privacy leakage during centralized AI's model training has drawn intensive concern from the public. A Parallel and Distributed Computing (or PDC) scheme, termed Federated Learning (FL), has emerged as a new paradigm to cope with the privacy issue by allowing clients to pe...