Federated Learning for Internet of Things

Abstract

The proliferation of the Internet of Things (IoT) and the advancements in machine learning (ML) have facilitated ubiquitous sensing and computing capabilities, enabling the interconnection of a wide array of devices to the Internet. Traditionally, data collection and data processing have been centralized, which may not be feasible due to issues such as long propagation delays, communication overload, and increasing data privacy concerns. To tackle these challenges, federated learning (FL) has emerged as a privacy-preserving distributed ML approach, allowing numerous devices to engage in model training without transferring their local data to a central server. This work presents a comprehensive review of FL as an approach to performing ML on distributed IoT data, with a specific emphasis on protecting data privacy and reducing communication costs associated with data transfer. The review encompasses various aspects, including the background of FL, the architecture of FL for IoT, the different types of FL for IoT, FL frameworks tailored for IoT, and diverse FL for IoT applications. Additionally, this paper outlines future research challenges and directions pertaining to FL for IoT. By embracing the potential of FL while addressing its challenges, IoT can benefit from reduced delays, improved communication efficiency, enhanced privacy preservation, and a more sustainable FL-IoT system.