HPFL-CN: Communication-Efficient Hierarchical Personalized Federated Edge Learning via Complex Network Feature Clustering

Abstract

Federated Learning (FL), a promising privacy-preserving distributed learning paradigm, has been extensively applied in urban environmental prediction tasks of Mobile Edge Computing (MEC) by training a global machine learning model without data sharing. However, it is hard for the shared global model to be well generalized among local edge servers, due to the statistical data heterogeneity, especially in real-world urban environmental data. Besides, the existing FL approaches may result in excessive communication and computation overhead due to the frequent transmission and aggregation of model parameters between massive edge servers and remote cloud servers. To address the above issues, we propose HPFL-CN, a novel communication-efficient Hierarchical Personalized Federated edge Learning framework via Complex Network feature clustering, aiming to cluster edge servers with similar environmental data distributions and then high-efficiently train personalized models for each cluster via hierarchical architecture. Specifically, HPFL-CN introduces Privacy-preserving Feature Clustering (PFC) to extract privacy-preserving low-dimensional feature representations of each edge server via mapping the environmental data to different complex network domains for clustering similar edge servers accurately. According to the clustering results of PFC, HPFL-CN further introduces an edge-mediator-cloud architecture for hierarchical model aggregation by Effective Hierarchical Scheduling (EHS), in which every mediator coordinates the training of edge servers within each cluster and periodically uploads model to cloud server for global model aggregation. Meanwhile, each mediator server would find a trade-off between cloud and edge models to realize personalization within clusters. Our extensive experiments on real-world datasets demonstrate the effectiveness and generalization of HPFL-CN, which outperforms other state-of-the-art FL methods regarding personalization performance and communication efficiency.