Federated learning review: Fundamentals, enabling technologies, and future applications
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Federated learning has issues with communication efficiency and system heterogeneity, as clients may have different computational resources or network connectivity, requiring careful design of the learning process.
“There are also issues with communication efficiency and system heterogeneity, as clients may have different computational resources or network connectivity, requiring the careful design of the learning process [14].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [14]
Federated learning requires robust mechanisms for ensuring secure model updates and preventing malicious actors from corrupting the global model, such as Federated Secure Aggregation or differential privacy.
“Additionally, FL requires robust mechanisms for ensuring secure model updates and preventing malicious actors from corrupting the global model, such as through Federated Secure Aggregation techniques or differential privacy to guarantee that updates cannot be reverse-engineered to reveal individual client data.”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [14]
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Flagged first, then by citations
Cited by
Sorted by citations
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- Authors
- Banabilah, S., Aloqaily, M., Alsayed, E., Malik, N., & Jararweh, Y.
- Affiliations
- Duquesne University (US)
- Mohamed bin Zayed University of Artificial Intelligence (AE)
- Corresponding author
- Jararweh, Yaser
- Venue
- Information Processing & Management
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 59
- Issue
- 6
- Pages
- 103061
- ISSN
- 0020-0271 (ISSN-L)
- 0306-4573
- OpenAlex
- W4293155325
- Semantic Scholar
- 4ab619779f7123fe85e789facb8a43b8fcb5d9c0
- Cited by
- 556
- References
- 158
- Citations by year
- 2026: 86 · 2025: 194 · 2024: 160 · 2023: 101 · 2022: 15
- FWCI
- 38.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Information Processing & Management (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- federated learning, on-device learning, privacy-preserving machine learning, Internet of Things, blockchain, autonomous vehicles, natural language processing, resource allocation, health care, recommender systems, battery management, fairness, personalization, public sector applications
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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