Federated Learning for Internet of Things: Applications, Challenges, and Opportunities

TL;DR

This paper reviews how federated learning can address privacy, communication, and storage challenges in IoT systems, highlighting opportunities, challenges, and recent solutions for deploying FL in IoT environments.

Contribution

It provides a comprehensive overview of the applications, challenges, and recent approaches of federated learning specifically tailored for IoT platforms.

Findings

Identifies seven critical challenges of FL in IoT.

Discusses recent promising approaches to address these challenges.

Highlights the potential of FL to enable diverse IoT applications.

Abstract

Billions of IoT devices will be deployed in the near future, taking advantage of faster Internet speed and the possibility of orders of magnitude more endpoints brought by 5G/6G. With the growth of IoT devices, vast quantities of data that may contain users' private information will be generated. The high communication and storage costs, mixed with privacy concerns, will increasingly challenge the traditional ecosystem of centralized over-the-cloud learning and processing for IoT platforms. Federated Learning (FL) has emerged as the most promising alternative approach to this problem. In FL, training data-driven machine learning models is an act of collaboration between multiple clients without requiring the data to be brought to a central point, hence alleviating communication and storage costs and providing a great degree of user-level privacy. However, there are still some challenges existing in the real FL system implementation on IoT networks. In this paper, we will discuss the opportunities and challenges of FL in IoT platforms, as well as how it can enable diverse IoT applications. In particular, we identify and discuss seven critical challenges of FL in IoT platforms and highlight some recent promising approaches towards addressing them.