Adversarial Attacks on Federated Learning Revisited: a Client-Selection Perspective
Xingyu Lyu, Shixiong Li, Ning Wang, Tao Li, Danjue Chen, Yimin Chen · 2024
Federated Learning (FL) is a widely adopted distributed machine learning technique where clients collaboratively train a model without sharing their data. A critical component of FL is client selection, which involves choosing the necessary number of clients for each training round. Current client selection algorithms for wireless FL rely on the conditions of wireless channels but do not account for vulnerabilities from attacks on these channels, such as channel state information (CSI) forgery attacks. In this paper, we introduce AirTrojan, a novel attack vector that targets client selection in FL. Our key insight is that since the channel state can be manipulated by attackers, an attacker can adjust their probability of being chosen as a participant. AirTrojan enhances the feasibility of adversarial attacks on FL, which usually assume that malicious clients are always selected as participants. We demonstrate the effectiveness of AirTrojan by showing how it can disrupt client selection and facilitate model poisoning attacks on FL. Our work highlights that it is urgent to add security components to client selection processes in wireless FL.