Robust Hierarchical Federated Learning with Anomaly Detection in Cloud-Edge-End Cooperation Networks

Federated learning (FL) enables devices to collaborate on machine learning (ML) model training with distributed data while preserving privacy. However, the traditional FL is inefficient and costly in cloud-edge-end cooperation networks since the adopted classical client-server communication framework fails to consider the real network structure. Moreover, malicious attackers and malfunctioning clients may be implied in all participators to exert adverse impacts as abnormal behaviours on the FL process. To address the above challenges, we leverage cloud-edge-end cooperation to propose a robust hierarchical federated learning (R-HFL) framework to enhance inherent system resistance to abnormal behaviours while improving communication efficiency in practical networks and keeping the advantages of the traditional FL. Specifically, we introduce a hierarchical cloud-edge-end collaboration-based FL framework to reduce communication costs. For the framework, we design a detection mechanism as partial cosine similarity (PCS) to filter adverse clients to improve performance, where the proposed lightweight technique has high computation parallelization. Besides, we theoretically discuss the influence of the proposed PCS on the convergence and stabilization of FL. Finally, the experimental results show that the proposed R-HFL always outperforms baselines in general cases under malicious attacks, which further shows the effectiveness of our scheme.

Automated summary

This study proposes a robust hierarchical federated learning (R-HFL) framework that utilizes cloud-edge cooperation to enhance communication efficiency and system resistance to abnormal behaviors in distributed machine learning training.