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

TLDR

Billions of IoT devices will soon be deployed, generating vast amounts of data that raise privacy concerns and impose high communication and storage costs, challenging centralized cloud learning. The article examines how federated learning can enable diverse IoT applications by outlining its opportunities, seven key challenges, and recent promising solutions. Federated learning trains models across multiple IoT clients without centralizing data, thereby reducing communication and storage burdens while preserving user privacy.

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 eco-system 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 article, we 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 toward addressing them.