TY - JOUR
T1 - Federated Learning for Internet of Things
T2 - A Comprehensive Survey
AU - Nguyen, Dinh C.
AU - Ding, Ming
AU - Pathirana, Pubudu N.
AU - Seneviratne, Aruna
AU - Li, Jun
AU - Vincent Poor, H.
N1 - Funding Information:
Manuscript received September 20, 2020; revised March 10, 2021; accepted April 12, 2021. Date of publication April 26, 2021; date of current version August 23, 2021. This work was supported in part by the CSIRO Data61, Australia, and in part by the U.S. National Science Foundation under Grant CCF-1908308. The work of Jun Li was supported by the National Natural Science Foundation of China under Grant 61872184. (Corresponding author: Dinh C. Nguyen.) Dinh C. Nguyen is with the School of Engineering, Deakin University, Waurn Ponds, VIC 3216, Australia, and also with Data61, CSIRO, Docklands, Melbourne, VIC 3008, Australia (e-mail: cdnguyen@deakin.edu.au).
Publisher Copyright:
© 1998-2012 IEEE.
PY - 2021/7/1
Y1 - 2021/7/1
N2 - 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.
AB - 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.
KW - Federated learning
KW - Internet of Things
KW - artificial intelligence
KW - machine learning
KW - privacy
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U2 - 10.1109/COMST.2021.3075439
DO - 10.1109/COMST.2021.3075439
M3 - Review article
AN - SCOPUS:85105036098
SN - 1553-877X
VL - 23
SP - 1622
EP - 1658
JO - IEEE Communications Surveys and Tutorials
JF - IEEE Communications Surveys and Tutorials
IS - 3
M1 - 9415623
ER -