Abstract
The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may not be feasible in realistic application scenarios due to the high scalability of modern IoT networks and growing data privacy concerns. Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need for data sharing. In this article, we provide a comprehensive survey of the emerging applications of FL in IoT networks, beginning from an introduction to the recent advances in FL and IoT to a discussion of their integration. Particularly, we explore and analyze the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing, and IoT privacy and security. We then provide an extensive survey of the use of FL in various key IoT applications such as smart healthcare, smart transportation, Unmanned Aerial Vehicles (UAVs), smart cities, and smart industry. The important lessons learned from this review of the FL-IoT services and applications are also highlighted. We complete this survey by highlighting the current challenges and possible directions for future research in this booming area.
Highlights
R ECENT years have witnessed the rapid development of the Internet of Things (IoT) which provides ubiquitous sensing and computing capabilities to connect a broad range of things to the Internet [1]
The popularity of IoT applications and services has become the main targets of malicious adversaries that can attack artificial intelligence (AI)/machine learning (ML) models integrated in IoT networks, by modifying data inputs or changing learning network weights which can lead to erroneous predicted outputs [74]
To avoid the privacy threat to vehicular IoT networks, a new approach is suggested in [102] using Federated Learning (FL) combined with local differential privacy which aims for perturbing gradients generated by vehicles while not compromising the utility of gradients
Summary
R ECENT years have witnessed the rapid development of the Internet of Things (IoT) which provides ubiquitous sensing and computing capabilities to connect a broad range of things to the Internet [1]. The use of third-party servers for AI training raises privacy concerns such as data breaches as the training data may contain sensitive information such as user addresses or personal preferences [5] It is highly necessary for developing innovative AI approaches to realize efficient and privacy-enhanced intelligent IoT networks and applications. By using FL, health data owners, e.g., hospitals, do not need to exchange their healthcare records with each other; instead, they train the AI model locally and only upload the trained parameters to the aggregator for global computation In this way, FL creates collaborative healthcare environments among different hospitals to accelerate patient diagnosis and treatment, without sacrificing user privacy. The success of recent FL-IoT applications makes the right time to draw attention to this prominent area of research
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