Stealthy Backdoor Attack in Federated Learning via Adaptive Layer-wise Gradient Alignment

The distributed nature of federated learning exposes it to significant security threats, among which backdoor attacks are one of the most prevalent. However, existing backdoor attacks face a trade-off between attack strength and stealthiness: attacks maximizing the attack strength are often detectable, while stealthier approaches significantly reduce the effectiveness of the attack itself. Both of them result in ineffective backdoor injection. In this paper, we propose an adaptive layer-wise gradient alignment strategy to effectively evade various robust defense mechanisms while preserving attack strength. Without requiring additional knowledge, we leverage the previous global update as a reference for alignment to ensure stealthiness during dynamic FL training. This fine-grained alignment strategy applies appropriate constraints to each layer, which helps to significantly maintain attack impact. To demonstrate the effectiveness of our method, we conduct exhaustive evaluations across a wide range of datasets and networks. Our experimental results show that the proposed attack effectively bypasses eight state-of-the-art (SOTA) defenses and achieves high backdoor accuracy, outperforming existing attacks by up to 54.76%. Additionally, it significantly preserves attack strength and maintains robust performance across diverse scenarios, highlighting its adaptability and generalizability.