Federated Learning for Internet of Things: A Comprehensive Survey

By Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Prasad Seneviratne, Jun Li, F. I. H. Vincent Poor. IEEE Communications Surveys and Tutorials, 2021

1,406 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/f03333b06c1b2e356dcc03f6bd3ef3d849d531e5

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Citations in the corpus

Federated Learning in Edge Computing: A Systematic Survey (2022, background): “[95] Analysis of FL’s potential for enabling a wide range of IoT services X X X X X X”
Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, neutral): “Additionally, by collecting large and diverse datasets from many devices, FL also improves the convergence rate of training and obtains an accurate global model [61].”
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges (2022, neutral): “In this sense, in 2020, Lim et al. [23] reviewed the use of CFL in mobile networks using different end-user applications, while in 2021, researchers such as Nguyen et al. [24] and Khan et al. [25] examined the application of FL in more complex scenarios involving different heterogeneous IoT devices.”
6G Internet of Things: A Comprehensive Survey (2021, background): “Nevertheless, the communication latency caused by packet loss checking over satellite channels has not been considered in the proposed multiuser satellite IoT system optimization.”
Heterogeneous Federated Learning: State-of-the-art and Research Challenges (2023, background): “[162] provide a survey and analysis of federated learning in IoT services (e.”