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Cited by 892Privacy-Preserving Technologies in DataIoT and Edge/Fog ComputingInternet of Things and AI

Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges

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Khan, Latif U., Saad, Walid, Han, Zhu, Hossain, Ekram, Hong, Choong Seon·2021·IEEE Communications Surveys & Tutorials
DOI 10.1109/comst.2021.3090430
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Abstract

The Internet of Things (IoT) will be ripe for the deployment of novel machine learning algorithm for both network and application management. However, given the presence of massively distributed and private datasets, it is challenging to use classical centralized learning algorithms in the IoT. To overcome this challenge, federated learning can be a promising solution that enables on-device machine learning without the need to migrate the private end-user data to a central cloud. In federated learning, only learning model updates are transferred between end-devices and the aggregation server. Although federated learning can offer better privacy preservation than centralized machine learning, it has still privacy concerns. In this paper, first, we present the recent advances of federated learning towards enabling federated learning-powered IoT applications. A set of metrics such as sparsification, robustness, quantization, scalability, security, and privacy, is delineated in order to rigorously evaluate the recent advances. Second, we devise a taxonomy for federated learning over IoT networks. Finally, we present several open research challenges with their possible solutions.

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  • Federated learning is a special kind of distributed learning that is characterized by various unstable users distributed in different places, each of whom has control over their devices and data.

    “Federated learning is a special kind of distributed learning that is characterized by various unstable users distributed in different places, each of whom has control over their devices and data [115, 116].”

    From Machine Unlearning: A Survey (2024) · cites this paper as [116]

    The Introduction (page unavailable) describes federated learning as on-device learning across geographically distributed locations, with data remaining on end-devices rather than moving to a central server. The Federated Learning Schemes section (page unavailable) also describes devices training on their local datasets. The cited passages do not establish that users are generally unstable or that each user controls their device and data, so those parts of the characterization are not supported. (LLM, conf=0.82)

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  151. Latif U. Khan is currently pursuing his Ph.D. degree in Computer Science and Engineering at Kyung Hee University (KHU), South Korea. He is working as a leading researcher in the intelligent Networking Laboratory under a project jointly funded by the prestigious Brain Korea 21st Century Plus and Min- istry of Science and ICT, South Korea. He received his MS (Electrical Engineering) degree with distinc- tion from University of Engineering and Technology (UET), Peshawar, Pakistan in 2017. Prior to joining the KHU, he has served as a faculty member and research associate in the UET, Peshawar, Pakistan. He has published his works in highly reputable conferences and journals. He is the author/co-author of two conference best paper awards. He is also author of two books, such as "Network Slicing for 5G and Beyond Networks" and "Federated Learning for Wireless Networks". His research interests include analytical techniques of optimization and game theory to edge computing, end-to-end network slicing, and federated learning for wireless networks.

Cited by

Sorted by citations

Authors
Khan, L. U., Saad, W., Han, Z., Hossain, E., & Hong, C. S.
Affiliations
  • Kyung Hee University (KR)
  • University of Manitoba (CA)
ORCID
Venue
IEEE Communications Surveys & Tutorials
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2021
Volume
23
Issue
3
Pages
1759-1799
ISSN
  • 1553-877X (ISSN-L)
  • 2373-745X
DOI
10.1109/comst.2021.3090430
OpenAlex
W3088682211
Cited by
892
References
20
Citations by year
2026: 122 · 2025: 201 · 2024: 202 · 2023: 190 · 2022: 130 · 2021: 45 · 2020: 2
FWCI
50.09
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Communications Surveys & Tutorials (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
National Research Foundation of Korea (NRF) grant funded by the Korea Government (2020R1A4A1018607)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
  • Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
Keywords
federated learning, Internet of Things, on-device learning, privacy preservation, sparsification, quantization, robustness, scalability, security (computing), open problems

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