Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness

Jinke Ren, Yinghui He, Dingzhu Wen, Guanding Yu*, Kaibin Huang, Dongning Guo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

95 Scopus citations


In cellular federated edge learning (FEEL), multiple edge devices holding local data jointly train a neural network by communicating learning updates with an access point without exchanging their data samples. With very limited communication resources, it is beneficial to schedule the most informative local learning updates. This paper focuses on FEEL with gradient averaging over participating devices in each round of communication. A novel scheduling policy is proposed to exploit both diversity in multiuser channels and diversity in the 'importance' of the edge devices' learning updates. First, a new probabilistic scheduling framework is developed to yield unbiased update aggregation in FEEL. The importance of a local learning update is measured by its gradient divergence. If one edge device is scheduled in each communication round, the scheduling policy is derived in closed form to achieve the optimal trade-off between channel quality and update importance. The probabilistic scheduling framework is then extended to allow scheduling multiple edge devices in each communication round. Numerical results obtained using popular models and learning datasets demonstrate that the proposed scheduling policy can achieve faster model convergence and higher learning accuracy than conventional scheduling policies that only exploit a single type of diversity.

Original languageEnglish (US)
Article number9170917
Pages (from-to)7690-7703
Number of pages14
JournalIEEE Transactions on Wireless Communications
Issue number11
StatePublished - Nov 2020


  • Federated edge learning
  • convergence analysis
  • multiuser diversity
  • resource management
  • scheduling

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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