Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning

Yuchang Sun, Zehong Lin, Yuyi Mao, Shi Jin, Jun Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review


Federated learning (FL) is a popular privacy-preserving distributed training scheme, where multiple devices collaborate to train machine learning models by uploading local model updates. To improve communication efficiency, over-the-air computation (AirComp) has been applied to FL, which leverages analog modulation to harness the superposition property of radio waves such that numerous devices can upload their model updates concurrently for aggregation. However, the uplink channel noise incurs considerable model aggregation distortion, which is critically determined by the device scheduling and compromises the learned model performance. In this paper, we propose a probabilistic device scheduling framework for over-the-air FL, named <italic>PO-FL</italic>, to mitigate the negative impact of channel noise, where each device is scheduled according to a certain probability and its model update is reweighted using this probability in aggregation. We prove the unbiasedness of this aggregation scheme and demonstrate the convergence of PO-FL on both convex and non-convex loss functions. Our convergence bounds unveil that the device scheduling affects the learning performance through the <italic>communication distortion</italic> and <italic>global update variance</italic>. Based on the convergence analysis, we further develop a channel and gradient-importance aware algorithm to optimize the device scheduling probabilities in PO-FL. Extensive simulation results show that the proposed PO-FL framework with channel and gradient-importance awareness achieves faster convergence and produces better models than baseline methods.

Original languageEnglish
Article number10341307
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Wireless Communications
Publication statusPublished - Dec 2023


  • Atmospheric modeling
  • channel awareness
  • Convergence
  • Data models
  • device scheduling
  • Federated learning (FL)
  • gradient importance
  • Optimal scheduling
  • over-the-air computation (AirComp)
  • Performance evaluation
  • Servers
  • Training

ASJC Scopus subject areas

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


Dive into the research topics of 'Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning'. Together they form a unique fingerprint.

Cite this