Cost-Efficient and Secure Federated Learning for Edge Computing
Zhuangzhuang Zhang, Libing Wu, Zhibo Wang, Jiahui Hu, Chao Ma, Qin Liu · IEEE Transactions on Mobile Computing · 2025
Due to the collaborative machine learning nature of Federated Learning (FL), it enables the training of machine learning models on large-scale distributed datasets in edge computing environments. Nevertheless, the application of FL in edge computing still faces three crucial challenges: resource constraint, privacy leakage, and Byzantine failures. Unfortunately, current approaches lack the ability to effectively balance these three challenges. In this paper, we propose FedEdge, a cost-efficient and secure FL for edge computing. FedEdge contains two main mechanisms: adaptive compression perturbation and dynamic update filtering. The adaptive compression perturbation mechanism reduces the communication overhead, provides different levels of privacy protection for edge nodes, and prevents Byzantine attacks. The dynamic update filtering mechanism is used to further filter Byzantine attacks and limit the impact of adaptive compression perturbation on the global model performance. The experimental results on the MNIST, CIFAR-10, CIFAR-100, and CelebA datasets demonstrate the effectiveness of FedEdge against free-riders, label-flipping, and sign-flipping attacks. Theoretical analysis also demonstrate that FedEdge can still converge even when the majority of edge nodes are malicious.