Update Aware Device Scheduling for Federated Learning at the Wireless Edge

Mohammad Mohammadi Amiri, Deniz Gunduz, Sanjeev R. Kulkarni, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We study federated learning (FL) at the wireless edge, where power-limited devices with local datasets train a joint model with the help of a remote parameter server (PS). We assume that the devices are connected to the PS through a bandwidth-limited shared wireless channel. At each iteration of FL, a subset of the devices are scheduled to transmit their local model updates to the PS over orthogonal channel resources. We design novel scheduling policies, that decide on the subset of devices to transmit at each round not only based on their channel conditions, but also on the significance of their local model updates. Numerical results show that the proposed scheduling policy provides a better long-term performance than scheduling policies based only on either of the two metrics individually. We also observe that when the data is independent and identically distributed (i.i.d.) across devices, selecting a single device at each round provides the best performance, while when the data distribution is non-i.i.d., more devices should be scheduled.

Original languageEnglish (US)
Title of host publication2020 IEEE International Symposium on Information Theory, ISIT 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2598-2603
Number of pages6
ISBN (Electronic)9781728164328
DOIs
StatePublished - Jun 2020
Event2020 IEEE International Symposium on Information Theory, ISIT 2020 - Los Angeles, United States
Duration: Jul 21 2020Jul 26 2020

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
Volume2020-June
ISSN (Print)2157-8095

Conference

Conference2020 IEEE International Symposium on Information Theory, ISIT 2020
CountryUnited States
CityLos Angeles
Period7/21/207/26/20

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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  • Cite this

    Amiri, M. M., Gunduz, D., Kulkarni, S. R., & Poor, H. V. (2020). Update Aware Device Scheduling for Federated Learning at the Wireless Edge. In 2020 IEEE International Symposium on Information Theory, ISIT 2020 - Proceedings (pp. 2598-2603). [9173960] (IEEE International Symposium on Information Theory - Proceedings; Vol. 2020-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIT44484.2020.9173960