Skip to main content

Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge

Author(s): Amiri, Mohammad Mohammadi; Gunduz, Deniz; Kulkarni, Sanjeev R; Poor, H Vincent

To refer to this page use:
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.
Publication Date: 27-Jan-2021
Citation: Amiri, Mohammad Mohammadi, Gunduz, Deniz, Kulkarni, Sanjeev R, Poor, H Vincent. (2021). Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge. IEEE Transactions on Wireless Communications, 20 (6), 3643 - 3658. doi:10.1109/twc.2021.3052681
DOI: doi:10.1109/twc.2021.3052681
ISSN: 1536-1276
EISSN: 1558-2248
Pages: 3643 - 3658
Type of Material: Journal Article
Journal/Proceeding Title: IEEE Transactions on Wireless Communications
Version: Author's manuscript

Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.