Yuming Tang1,#, Yitian Zhang2,#, Tao Niu1, Zhen Li2,3,*, Zijian Zhang1,3, Huaping Chen4, Long Zhang4
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2451-2477, 2024, DOI:10.32604/cmes.2024.030084
- 11 March 2024
Abstract Federated Learning (FL), as an emergent paradigm in privacy-preserving machine learning, has garnered significant interest from scholars and engineers across both academic and industrial spheres. Despite its innovative approach to model training across distributed networks, FL has its vulnerabilities; the centralized server-client architecture introduces risks of single-point failures. Moreover, the integrity of the global model—a cornerstone of FL—is susceptible to compromise through poisoning attacks by malicious actors. Such attacks and the potential for privacy leakage via inference starkly undermine FL’s foundational privacy and security goals. For these reasons, some participants unwilling use their private data… More >