The explosive growth of smart devices (e.g., mobile phones, vehicles, drones) with sensing, communication, and computation capabilities gives rise to an unprecedented amount of data. The generated massive data together with the rapid advancement of machine learning (ML) techniques spark a variety of intelligent applications. To distill intelligence for supporting these applications, federated learning (FL) emerges as an effective distributed ML framework, given its potential to enable privacy-preserving model training at the network edge. In this article, we discuss the challenges and solutions of achieving scalable wireless FL from the perspectives of both network design and resource orchestration. For network design, we discuss how task-oriented model aggregation affects the performance of wireless FL, followed by proposing effective wireless techniques to enhance the communication scalability via reducing the model aggregation distortion and improving the device participation. For resource orchestration, we identify the limitations of the existing optimization-based algorithms and propose three task-oriented learning algorithms to enhance the algorithmic scalability via achieving computation-efficient resource allocation for wireless FL. We highlight several potential research issues that deserve further study.
@article{arxiv.2310.05076,
title = {Towards Scalable Wireless Federated Learning: Challenges and Solutions},
author = {Yong Zhou and Yuanming Shi and Haibo Zhou and Jingjing Wang and Liqun Fu and Yang Yang},
journal= {arXiv preprint arXiv:2310.05076},
year = {2023}
}
Comments
This paper has been accepted by IEEE Internet of Things Magazine