Asynchronous Semi-Decentralized Federated Edge Learning for Heterogeneous Clients

Yuchang Sun, Jiawei Shao, Yuyi Mao, Jun Zhang

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

2 Citations (Scopus)


Federated edge learning (FEEL) has drawn much attention as a privacy-preserving distributed learning framework for mobile edge networks. In this work, we investigate a novel semi-decentralized FEEL (SD-FEEL) architecture where multiple edge servers collaborate to incorporate more data from edge devices in training. Despite the low training latency enabled by fast edge aggregation, the device heterogeneity in computational resources deteriorates the efficiency. This paper proposes an asynchronous training algorithm to overcome this issue in SD-FEEL, where edge servers are allowed to independently set deadlines for the associated client nodes and trigger the model aggregation. To deal with different levels of model staleness, we design a staleness-aware aggregation scheme and analyze its convergence. Simulation results demonstrate the effectiveness of our proposed algorithm in achieving faster convergence and better learning performance than synchronous training.

Original languageEnglish
Title of host publicationICC 2022 - IEEE International Conference on Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538683477
Publication statusPublished - Aug 2022
Event2022 IEEE International Conference on Communications, ICC 2022 - Seoul, Korea, Republic of
Duration: 16 May 202220 May 2022

Publication series

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


Conference2022 IEEE International Conference on Communications, ICC 2022
Country/TerritoryKorea, Republic of


  • asynchronous training
  • device heterogeneity
  • Federated learning (FL)
  • mobile edge computing (MEC)

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Asynchronous Semi-Decentralized Federated Edge Learning for Heterogeneous Clients'. Together they form a unique fingerprint.

Cite this