CoBA: Collusive Backdoor Attacks With Optimized Trigger to Federated Learning

Xiaoting Lyu, Yufei Han, Wei Wang, Jingkai Liu, Bin Wang, Kai Chen, Yidong Li, Jiqiang Liu, Xiangliang Zhang

Abstract: Considerable efforts have been devoted to addressing distributed backdoor attacks in federated learning (FL) systems. While significant progress has been made in enhancing the security of FL systems, our study reveals that there remains a false sense of security surrounding FL. We demonstrate that colluding malicious participants can effectively execute backdoor attacks during the FL training process, exhibiting high sparsity and stealthiness, which means they can evade common defense methods with only a few attack iterations. Our research highlights this vulnerability by proposing a Collusive Backdoor Attack named CoBA. CoBA is designed to enhance the sparsity and stealthiness of backdoor attacks by offering trigger tuning to facilitate learning of backdoor training data, controlling the bias of malicious local model updates, and applying the projected gradient descent technique. By conducting extensive empirical studies on 5 benchmark datasets, we make the following observations: 1) CoBA successfully circumvents 15 state-of-the-art defense methods for robust FL; 2) Compared to existing backdoor attacks, CoBA consistently achieves superior attack performance; and 3) CoBA can achieve persistent poisoning effects through significantly sparse attack iterations. These findings raise substantial concerns regarding the integrity of FL and underscore the urgent need for heightened vigilance in defending against such attacks.