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

Zijian Li, Zihan Chen, Xiaohui Wei, Shang Gao, Chenghao Ren, Tony Q.S. Quek

研究成果: Conference contribution

5 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題2022 19th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2022
發行者IEEE Computer Society
頁面325-333
頁數9
ISBN(電子)9781665486439
DOIs
出版狀態Published - 2022
事件19th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2022 - Virtual, Online, Sweden
持續時間: 2022 9月 202022 9月 23

出版系列

名字Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops
2022-September
ISSN(列印)2155-5486
ISSN(電子)2155-5494

Conference

Conference19th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2022
國家/地區Sweden
城市Virtual, Online
期間22-09-2022-09-23

All Science Journal Classification (ASJC) codes

  • 電腦網路與通信
  • 硬體和架構
  • 電氣與電子工程

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