  In today's data-driven landscape, the delicate equilibrium between
safeguarding user privacy and unleashing data 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
decentralized 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
decentralized 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.
