@inproceedings{
huang2024parameter,
title={Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning},
author={Wenke Huang and Mang Ye and Zekun Shi and Guancheng Wan and He Li and Bo Du},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=g8wnC1E1OS}
}
Backdoor attacks pose a serious threat to federated systems, where malicious clients optimize on the triggered distribution to mislead the global model towards a predefined target. Existing backdoor defense methods typically require either homogeneous assumption, validation datasets, or client optimization conflicts. In our work, we observe that benign heterogeneous distributions and malicious triggered distributions exhibit distinct parameter importance degrees. We introduce the Fisher Discrepancy Cluster and Rescale (FDCR) method, which utilizes Fisher Information to calculate the degree of parameter importance for local distributions. This allows us to reweight client parameter updates and identify those with large discrepancies as backdoor attackers. Furthermore, we prioritize rescaling important parameters to expedite adaptation to the target distribution, encouraging significant elements to contribute more while diminishing the influence of trivial ones. This approach enables FDCR to handle backdoor attacks in heterogeneous federated learning environments. Empirical results on various heterogeneous federated scenarios under backdoor attacks demonstrate the effectiveness of our method.
Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning
Wenke Huang Mang Ye Zekun Shi Guancheng Wan He Li Bo Du