Convergence of update aware device scheduling for federated learning at the wireless edge
File(s)
Accepted version
Author(s)
Amiria, Mohammad Mohammadi
Gunduzb, Deniz
Kulkarni, Sanjeev R
Vincent Poor, H
Type
Journal Article
Abstract
We study federated learning (FL) at the wireless edge, where power-limited devices with local datasets collaboratively 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, while each participating device must compress its model update to accommodate to its link capacity. We design novel scheduling and resource allocation policies that decide on the subset of the devices to transmit at each round, and how the resources should be allocated among the participating devices, not only based on their channel conditions, but also on the significance of their local model updates. We then establish convergence of a wireless FL algorithm with device scheduling, where devices have limited capacity to convey their messages. The results of numerical experiments show that the proposed scheduling policy, based on both the channel conditions and the significance of the local model updates, provides a better long-term performance than scheduling policies based only on either of the two metrics individually. Furthermore, we 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., scheduling multiple devices at each round improves the performance. This observation is verified by the convergence result, which shows that the number of scheduled devices should increase for a less diverse and more biased data distribution.
Date Issued
2021-06-01
Date Acceptance
2021-01-01
Citation
IEEE Transactions on Wireless Communications, 2021, 20, pp.3643-3658
ISSN
1536-1276
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Start Page
3643
End Page
3658
Journal / Book Title
IEEE Transactions on Wireless Communications
Volume
20
Copyright Statement
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Commission of the European Communities
Identifier
https://ieeexplore.ieee.org/document/9337227
Grant Number
677854
Subjects
Networking & Telecommunications
0805 Distributed Computing
0906 Electrical and Electronic Engineering
1005 Communications Technologies
Publication Status
Published
Date Publish Online
2021-01-27