Resource consumption for supporting federated learning in wireless networks

Liu, Y.-J., Qin, S., Sun, Y. and Feng, G. (2022) Resource consumption for supporting federated learning in wireless networks. IEEE Transactions on Wireless Communications, 21(11), pp. 9974-9989. (doi: 10.1109/TWC.2022.3181611)

Abstract

Federated learning (FL) has recently become one of the hottest focuses in wireless edge networks with the ever-increasing computing capability of user equipment (UE). In FL, UEs train local machine learning models and transmit them to an aggregator, where a global model is formed and then sent back to UEs. In wireless networks, local training and model transmission can be unsuccessful due to constrained computing resources, wireless channel impairments, bandwidth limitations, etc., which degrades FL performance in model accuracy and/or training time. Moreover, we need to quantify the benefits and cost of deploying edge intelligence, as model training and transmission consume certain amount of resources. Therefore, it is imperative to deeply understand the relationship between FL performance and multiple-dimensional resources. In this paper, we construct an analytical model to investigate the relationship between the FL model accuracy and consumed resources in FL empowered wireless edge networks. Based on the analytical model, we explicitly quantify the model accuracy, available computing resources and communication resources. Numerical results validate the effectiveness of our theoretical modeling and analysis, and demonstrate the trade-off between the communication and computing resources for achieving a certain model accuracy.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Sun, Dr Yao
• Authors: Liu, Y.-J., Qin, S., Sun, Y., and Feng, G.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IEEE Transactions on Wireless Communications
• Publisher: IEEE
• ISSN: 1536-1276
• ISSN (Online): 1558-2248
• Published Online: 16 June 2022
• Copyright Holders: Copyright © 2022 IEEE
• First Published: First published in IEEE Transactions on Wireless Communications 21(11): 9974-9989
• Publisher Policy: Reproduced in accordance with the publisher copyright policy