Client dependability evaluation in federated learning framework

Kexiu Han; Guoyue Zhang; Liangbin Yang; Jing Bai

doi:10.1117/12.3058096

3 January 2025 Client dependability evaluation in federated learning framework

Kexiu Han, Guoyue Zhang, Liangbin Yang, Jing Bai

Author Affiliations +

Proceedings Volume 13519, Third International Conference on Communications, Information System, and Data Science (CISDS 2024); 135190A (2025) https://doi.org/10.1117/12.3058096
Event: Third International Conference on Communications, Information System and Data Science 2024, 2024, Nanjing, China

Abstract

Federated learning (FL) is a widely adopted distributed machine learning paradigm, individual clients train local models by using their private datasets and then send model updates to a central server. While its decentralized training process can protect data privacy, it is vulnerable to attacks such as model poisoning attack and backdoor attack. The effect of malicious clients can be mitigated by applying robust FL methods. However, most existing solutions ignored the client dependability. This paper explores a method for quantitatively assessing the client dependability in FL framework. Firstly, based on semi- Markov process (SMP), we build a multi-dimensional evaluation model for understanding how the client's behaviors under attack and its recovery behaviors affect the client dependability. Then, we deduce the formulas of calculating the availability, security risk and reliability in order to analyze the quantitative relationship between different factors and the client dependability from these three perspectives. Furthermore, we perform numerical analysis to investigate how different system parameters impact the client dependability.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kexiu Han, Guoyue Zhang, Liangbin Yang, and Jing Bai "Client dependability evaluation in federated learning framework", Proc. SPIE 13519, Third International Conference on Communications, Information System, and Data Science (CISDS 2024), 135190A (3 January 2025); https://doi.org/10.1117/12.3058096

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