Smart City Gnosys
Smart city article details
| Title | Federated Learning For Internet Of Things: A Comprehensive Survey |
|---|---|
| ID_Doc | 26346 |
| Authors | Nguyen D.C.; Ding M.; Pathirana P.N.; Seneviratne A.; Li J.; Vincent Poor H. |
| Year | 2021 |
| Published | IEEE Communications Surveys and Tutorials, 23, 3 |
| DOI | http://dx.doi.org/10.1109/COMST.2021.3075439 |
| 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. © 1998-2012 IEEE. |
| Author Keywords | artificial intelligence; Federated learning; Internet of Things; machine learning; privacy |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
1161  | 0.977 | Kishor K.; Quezada A.; Yadav S.P. | A Critical Role For Federated Learning In Iot | Federated Learning for Smart Communication using IoT Application (2024) |
| 26365 | 0.955 | Aggarwal M.; Khullar V.; Rani S.; André Prola T.; Bhattacharjee S.B.; Shawon S.M.; Goyal N. | Federated Learning On Internet Of Things: Extensive And Systematic Review | Computers, Materials and Continua, 79, 2 (2024) |
| 26338 | 0.926 | Quan M.K.; Pathirana P.N.; Wijayasundara M.; Setunge S.; Nguyen D.C.; Brinton C.G.; Love D.J.; Poor H.V. | Federated Learning For Cyber Physical Systems: A Comprehensive Survey | IEEE Communications Surveys and Tutorials (2025) |
| 756 | 0.923 | Anitha G.; Jegatheesan A.; Baburaj E. | A Comparative Analysis Of Federated Learning Towards Big Data Iot With Future Perspectives | 2023 3rd International Conference on Computing and Information Technology, ICCIT 2023 (2023) |
| 924 | 0.921 | Mehdi M.; Makkar A.; Conway M. | A Comprehensive Review Of Open-Source Federated Learning Frameworks | Procedia Computer Science, 260 (2025) |
| 26352 | 0.918 | Pandya S.; Srivastava G.; Jhaveri R.; Babu M.R.; Bhattacharya S.; Maddikunta P.K.R.; Mastorakis S.; Piran M.J.; Gadekallu T.R. | Federated Learning For Smart Cities: A Comprehensive Survey | Sustainable Energy Technologies and Assessments, 55 (2023) |
| 8396 | 0.918 | Sharma P.; Kashniyal J.; Esham E. | An Insight Into Federated Learning: A Collaborative Approach For Machine Learning | 2024 4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024 (2024) |
| 26362 | 0.909 | Jiang J.C.; Kantarci B.; Oktug S.; Soyata T. | Federated Learning In Smart City Sensing: Challenges And Opportunities | Sensors (Switzerland), 20, 21 (2020) |
| 19206 | 0.908 | Thakur A.; Tyagi R.; Tripathy H.K.; Yang T.; Rathore R.S.; Mo D.; Wang L. | Detecting Network Attack Using Federated Learning For Iot Devices | International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024 (2024) |
| 23947 | 0.908 | Kapoor A.; Kumar D. | Enhancing Smart Cities With Federated Learning: A Framework For Secure, Scalable, And Intelligent Urban Sensing Systems | IEEE Internet of Things Magazine (2025) |