Federated Learning with Downlink Device Selection

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

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

Abstract

We study federated edge learning, where a global model is trained collaboratively using privacy-sensitive data at the edge of a wireless network. A parameter server (PS) keeps track of the global model and shares it with the wireless edge devices for training using their private local data. The devices then transmit their local model updates, which are used to update the global model, to the PS. The algorithm, which involves transmission over PS-to-device and device-to-PS links, continues until the convergence of the global model or lack of any participating devices. In this study, we consider device selection based on downlink channels over which the PS shares the global model with the devices. Performing digital downlink transmission, we design a partial device participation framework where a subset of the devices is selected for training at each iteration. Therefore, the participating devices can have a better estimate of the global model compared to the full device participation case which is due to the shared nature of the broadcast channel with the price of updating the global model with respect to a smaller set of data. At each iteration, the PS broadcasts different quantized global model updates to different participating devices based on the last global model estimates available at the devices. We investigate the best number of participating devices through experimental results for image classification using the MNIST dataset with biased distribution.

Original languageEnglish (US)
Title of host publication2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages306-310
Number of pages5
ISBN (Electronic)9781665428514
DOIs
StatePublished - 2021
Event22nd IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2021 - Lucca, Italy
Duration: Sep 27 2021Sep 30 2021

Publication series

NameIEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume2021-September

Conference

Conference22nd IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2021
Country/TerritoryItaly
CityLucca
Period9/27/219/30/21

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Information Systems

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