SiftFL: Scheduling-Based Robust Backdoor Detection in Federated Learning

Abstract

Backdoor attacks pose a serious threat to federated learning, particularly when client data are non-IID and the attacker ratio is high. FilterFL is a recent server-side defense that employs two Conditional Generative Adversarial Networks (CGANs) to generate synthetic samples and identify malicious client models without requiring clean server data. However, executing both CGAN stages in every communication round makes the defense robust but computationally expensive. In this paper, we propose SiftFL, a scheduling-based robust backdoor detection method that sifts out malicious client models at a fraction of the original cost. SiftFL decouples the cost of the CGAN stages from the number of communication rounds by executing them periodically rather than every round and complements this schedule with a trust history score that stabilizes client filtering across rounds. This design preserves and, in several settings, improves the robustness of CGAN-based detection while sharply lowering its server-side cost. Experiments using MNIST, CIFAR-10, and GTSRB benchmark dataset show that SiftFL reduces server defense computation by up to 99% while keeping the drop in main accuracy within about 4% in the most challenging non-IID cases compared to the original baseline. At the same time, the attack success rate is reduced by roughly 97–99%, and robustness accuracy improves significantly to 85%, in settings where the original FilterFL becomes unstable. The results indicate that scheduling and trust history make SiftFL a more practical and reliable backdoor detection method under non-IID data distribution and high attacker presence.