Client-Edge-Cloud Hierarchical Federated Learning

Lumin Liu, Jun Zhang, S. H. Song, Khaled B. Letaief

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

338 Citations (Scopus)


Federated Learning is a collaborative machine learning framework to train a deep learning model without accessing clients' private data. Previous works assume one central parameter server either at the cloud or at the edge. The cloud server can access more data but with excessive communication overhead and long latency, while the edge server enjoys more efficient communications with the clients. To combine their advantages, we propose a client-edge-cloud hierarchical Federated Learning system, supported with a HierFAVG algorithm that allows multiple edge servers to perform partial model aggregation. In this way, the model can be trained faster and better communication-computation trade-offs can be achieved. Convergence analysis is provided for HierFAVG and the effects of key parameters are also investigated, which lead to qualitative design guidelines. Empirical experiments verify the analysis and demonstrate the benefits of this hierarchical architecture in different data distribution scenarios. Particularly, it is shown that by introducing the intermediate edge servers, the model training time and the energy consumption of the end devices can be simultaneously reduced compared to cloud-based Federated Learning.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Communications, ICC 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728150895
Publication statusPublished - Jun 2020
Event2020 IEEE International Conference on Communications, ICC 2020 - Dublin, Ireland
Duration: 7 Jun 202011 Jun 2020

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607


Conference2020 IEEE International Conference on Communications, ICC 2020


  • Edge Learning
  • Federated Learning
  • Mobile Edge Computing

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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