Multi-Core Federated Learning for Mobile Edge Computing Platforms

Yang Bai, Lixing Chen, Jianhua Li, Jun Wu, Pan Zhou, Zichuan Xu, Jie Xu

Research output: Contribution to journalArticlepeer-review


With increasingly strict data privacy regulations, Federated Learning (FL) has become one of the most often heard machine learning techniques due to its privacy-preserving trait. To efficiently implement the FL intelligence, researchers recently resort to a newly-emerged computing paradigm, Mobile Edge Computing (MEC), and bring about a burst of works. However, most existing works neglect practical issues in MEC systems, e.g., device heterogeneity, unstable channel conditions, and unknown user mobility. Any of them, if not handled properly, can cause fatal failures to FL. This paper proposed a novel FL framework, called Multi-Core Federated Learning (MC-FL), to help FL intelligence land successfully on realistic MEC systems. A distinct feature of MC-FL is maintaining and training multiple global models that exhibit different tradeoffs between learning performances and computational complexity. While this modification seems simple, it can effectively handle the device heterogeneity and device status variations, and improve the compatibility and robustness of FL. Further, MC-FL employs a partial client participation scheme that allows participating clients to vary across time. This enables MC-FL to function under uncertain mobile environments. We rigorously prove the convergence of the designed MC-FL framework. In particular, we propose an online client scheduling scheme for MC-FL to judiciously schedule clients for training multiple global models in a manner that minimizes the completion time of MC-FL. We also provide a service provisioning scenario with MC-FL to show how service subscribers could benefit from multiple GMs and improve their quality of experience (QoE). We evaluate our method on real-world datasets, and the results show that MC-FL outperforms state-of-the-art benchmarks.

Original languageEnglish
Pages (from-to)1
Number of pages1
JournalIEEE Internet of Things Journal
Publication statusAccepted/In press - 2022
Externally publishedYes


  • client scheduling
  • federated learning
  • Mobile edge computing

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


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