Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation

• Published in: IEEE transactions on parallel and distributed systems Vol. 33; no. 10; pp. 2401 - 2415
• Main Authors: Li, Jun, Shao, Yumeng, Wei, Kang, Ding, Ming, Ma, Chuan, Shi, Long, Han, Zhu, Poor, H. Vincent
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Agglomeration; Blockchain; Blockchains; Clients; Computational modeling; computing resource allocation; Cryptography; Data processing; Federated learning; lazy client; Machine learning; Malfunctions; Optimization; Performance evaluation; Privacy; Resource allocation; Resource management; Servers; Training; Upper bound; Upper bounds

Federated learning (FL), as a distributed machine learning paradigm, promotes personal privacy by local data processing at each client. However, relying on a centralized server for model aggregation, standard FL is vulnerable to server malfunctions, untrustworthy servers, and external attacks. To ad...