Federated Learning Backdoor Defense Based on Watermark Integrity

As the application of federated learning becomes widespread, security issues, especially the threat of backdoor at-tacks, become increasingly prominent. Current defense measures against backdoor poisoning in federated learning are less effective when the proportion of poisoned data injected by malicious participants exceeds 50 %. To address this issue, we propose a backdoor defense method for federated learning based on model watermarking. This method generates an initial global model with a watermark through the aggregation server and distributes it to local servers. By using the integrity of the returned wa-termark, it detects malicious participants, effectively enhancing the robustness of the global model. Experiments conducted on the CIFAR-10 and Tiny-ImageNet datasets demonstrate that our method can effectively detect and defend against backdoor poisoning attacks under a high proportion of poisoned data, as well as different triggers, attack methods, and scales.