Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning

Taejin Kim, Jiarui Li, Nikhil Madaan, Shubhranshu Singh, Carlee Joe-Wong

Keywords: Federated Learning, Adversarial Training, Backdoor Attack

TL;DR: Novel threat exploiting the intersection of federated adversarial training and backdoor attacks

Abstract: In today's data-driven landscape, the delicate equilibrium between safeguarding user privacy and unleashing data's potential stands as a paramount concern. Federated learning, which enables collaborative model training without necessitating data sharing, has emerged as a privacy-centric solution. This distributed approach brings forth security challenges, notably poisoning and backdoor attacks where malicious entities inject corrupted data. Our research, initially spurred by test-time evasion attacks, investigates the intersection of adversarial training and backdoor attacks within federated learning, introducing Adversarial Robustness Unhardening (ARU). ARU is employed by a subset of adversaries to intentionally undermine model robustness during federated training, rendering models susceptible to a broader range of evasion attacks. We present extensive empirical experiments evaluating ARU's impact on adversarial training and existing robust aggregation defenses against poisoning and backdoor attacks. Our findings inform strategies for enhancing ARU to counter current defensive measures and highlight the limitations of existing defenses, offering insights into bolstering defenses against ARU.

Submission Number: 19