Hwamei: A Learning-Based Synchronization Scheme for Hierarchical Federated Learning

Tianyu Qi, Yufeng Zhan, Peng Li, Jingcai Guo, Yuanqing Xia

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

5 Citations (Scopus)

Abstract

Federated learning (FL) enables collaborative model training among distributed devices without data sharing, but existing FL suffers from poor scalability because of global model synchronization. To address this issue, hierarchical federated learning (HFL) has been recently proposed to let edge servers aggregate models of devices in proximity, while synchronizing via the cloud periodically. However, a critical open challenge about how to design a good synchronization scheme (when devices and edges should be synchronized) is still unsolved. Devices are heterogeneous in computing and communication capability, and their data could be non-IID. No existing work can well synchronize various roles (e.g., devices and edge) in HFL to guarantee high learning efficiency and accuracy. In this paper, we propose a learning-based synchronization scheme for HFL systems. By collecting data such as edge models, CPU usage, communication time, etc., we design a deep reinforcement learning-based approach to decide the frequencies of cloud aggregation and edge aggregation, respectively. The proposed scheme well considers device heterogeneity, non-IID data and device mobility, to maximize the training model accuracy while minimizing the energy overhead. We build an HFL testbed and conduct experiments using real data obtained from Raspberry Pi and Alibaba Cloud. Extensive experimental results have confirmed the effectiveness of Hwamei.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE 43rd International Conference on Distributed Computing Systems, ICDCS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages534-544
Number of pages11
ISBN (Electronic)9798350339864
DOIs
Publication statusPublished - Oct 2023
Event43rd IEEE International Conference on Distributed Computing Systems, ICDCS 2023 - Hong Kong, China
Duration: 18 Jul 202321 Jul 2023

Publication series

NameProceedings - International Conference on Distributed Computing Systems
Volume2023-July

Conference

Conference43rd IEEE International Conference on Distributed Computing Systems, ICDCS 2023
Country/TerritoryChina
CityHong Kong
Period18/07/2321/07/23

Keywords

  • deep reinforcement learning
  • hierarchical federated learning
  • statistical heterogeneity
  • system heterogeneity

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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