The DOI matches its Crossref record; 129 of the 130 references it cites were found in a registry or an index.
A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond
Abstract
Driven by privacy concerns and the visions of deep learning, the last four years have witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An emerging model, called federated learning (FL), is rising above both centralized systems and on-site analysis, to be a new fashioned design for ML implementation. It is a privacy-preserving decentralized approach, which keeps raw data on devices and involves local ML training while eliminating data communication overhead. A federation of the learned and shared models is then performed on a central server to aggregate and share the built knowledge among participants. This article starts by examining and comparing different ML-based deployment architectures, followed by in-depth and in-breadth investigation on FL. Compared to the existing reviews in the field, we provide in this survey a new classification of FL topics and research fields based on thorough analysis of the main technical challenges and current related work. In this context, we elaborate comprehensive taxonomies covering various challenging aspects, contributions, and trends in the literature, including core system models and designs, application areas, privacy and security, and resource management. Furthermore, we discuss important challenges and open research directions toward more robust FL systems.
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“This is because when the data is Non-IID it can bias the model more, especially when trained with SGD [79].”
From Poisoning attacks in federated learning: A survey (2023) · cites this paper as [79]
Citations
Cites
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Advances and Open Problems in Federated Learning
Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772
The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781
Federated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322
Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2975749 · Cited by 6,210
The MovieLens Datasets: History and Context
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Sharafaldin, I., Habibi Lashkari, A., & Ghorbani, A. A. (2018) · Proceedings of the 4th International Conference on Information Systems Security and Privacy · doi:10.5220/0006639801080116 · Cited by 5,318
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Cited by
Sorted by citations
Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges
Khan, L. U., Saad, W., Han, Z., Hossain, E., & Hong, C. S. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3090430 · Cited by 892
Wahab, O. A., Mourad, A., Otrok, H., & Taleb, T. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3058573 · Cited by 506
Self-citationGhimire, B., & Rawat, D. B. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3150363 · Cited by 520
A state-of-the-art survey on solving non-IID data in Federated Learning
Ma, X., Zhu, J., Lin, Z., Chen, S., & Qin, Y. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.05.003 · Cited by 435
Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3118642 · Cited by 342
Metadata
- Authors
- Abdulrahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., & Guizani, M.
- Affiliations
- École de Technologie Supérieure (CA)
- Lebanese American University (LB)
- Qatar University (QA)
- Venue
- IEEE Internet of Things Journal
- Published
- 2021-04-01
- Volume
- 8
- Issue
- 7
- Pages
- 5476-5497
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W3135231128
- Semantic Scholar
- 53ec46e17af3009033a7b62d3f4b77371bb406fd
- Cited by
- 796
- References
- 130
- Citations by year
- 2026: 85 · 2025: 177 · 2024: 189 · 2023: 192 · 2022: 104 · 2021: 49
- FWCI
- 33.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- Espace ÉTS (ETS) (submittedVersion)
- Qatar University QSpace (Qatar University) (submittedVersion)
- Funders
- MITACS
- Ericsson Canada
- ETS Montreal
- Lebanese American University
- 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 › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Keywords
- federated learning, privacy-preserving machine learning, decentralized learning, on-device learning, model aggregation, data privacy, resource management, federated learning security, federated learning architecture, Internet of Things
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