A Secure and Fair Client Selection Based on DDPG for Federated Learning

Abstract

Federated learning (FL) is a machine learning technique in which a large number of clients collaborate to train models without sharing private data. However, FL’s integrity is vulnerable to unreliable models; for instance, data poisoning attacks can compromise the system. In addition, system preferences and resource disparities preclude fair participation by reliable clients. To address this challenge, we propose a novel client selection strategy that introduces a security-fairness value to measure client performance in FL. The value in question is a composite metric that combines a security score and a fairness score. The former is dynamically calculated from a beta distribution reflecting past performance, while the latter considers the client’s participation frequency in the aggregation process. The weighting strategy based on the deep deterministic policy gradient (DDPG) determines these scores. Experimental results confirm that our method fairly effectively selects reliable clients and maintains the security and fairness of the FL system.