FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning

Federated Learning (FL) is a distributed learning paradigm that enables different parties to train a model together for high quality and strong privacy protection. In this scenario, individual participants may get compromised and perform backdoor attacks by poisoning the data (or gradients). Existing work on robust aggregation and certified FL robustness does not study how hardening benign clients can affect the global model (and the malicious clients). In this work, we theoretically analyze the connection among cross-entropy loss, attack success rate, and clean accuracy in this setting. Moreover, we propose a trigger reverse engineering based defense and show that our method can achieve robustness improvement with guarantee (i.e., reducing the attack success rate) without affecting benign accuracy. We conduct comprehensive experiments across different datasets and attack settings. Our results on nine competing SOTA defense methods show the empirical superiority of our method on both single-shot and continuous FL backdoor attacks. Code is available at https://github.com/KaiyuanZh/FLIP . INTRODUCTION Federated Learning (FL) is a distributed learning paradigm with many applications, such as next word prediction (McMahan et al., 2017 ), credit prediction (Cheng et al., 2021a), and IoT device aggregation (Samarakoon et al., 2018) . FL promises scalability and privacy as its training is distributed to many clients. Due to the decentralized nature of FL, recent studies demonstrate that individual participants may be compromised and become susceptible to backdoor attacks (