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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Non-IID data can bias the model more, especially when trained with SGD.
“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]
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- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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