A Review of Client Selection Methods in Federated Learning
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A fair client selection algorithm design can prevent malicious clients from participating in training using techniques such as authentication and reputation assessment, reducing the harm of malicious attacks on the model.
“Meanwhile, a fair client selection algorithm design can also prevent malicious clients from participating in training using techniques such as authentication and reputation assessment, reducing the harm of malicious attacks on the model (Mayhoub and M. Shami T, 2023).”
From Survey: federated learning data security and privacy-preserving in edge-Internet of Things (2024) · cites this paper as Mayhoub and M. Shami T, 2023
Cites
Federated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Vincent Poor, H. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3075439 · Cited by 1,503 · cited 2× · see citing works
“Increase learning quality [6]: using diverse datasets of various devices leads to increase the accuracy of the learned final global model and increase the learning performance [6, 7].”
Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems
Bubeck, S., & Cesa-Bianchi, N. (2012) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000024 · Cited by 1,295 · see citing works
Adaptive Federated Optimization
Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečný, J., Kumar, S., & McMahan, H. B. (2020) · arXiv · doi:10.48550/arxiv.2003.00295 · Cited by 840 · see citing works
Abdulrahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., & Guizani, M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3030072 · Cited by 796 · see citing works
Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications
Aledhari, M., Razzak, R., Parizi, R. M., & Saeed, F. (2020) · IEEE Access · doi:10.1109/access.2020.3013541 · Cited by 733 · see citing works
Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues
Du, Z., Wu, C., Yoshinaga, T., Yau, K.-L. A., Ji, Y., & Li, J. (2020) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2020.2992630 · Cited by 393 · see citing works
Challenges and future directions of secure federated learning: a survey
Zhang, K., Song, X., Zhang, C., & Yu, S. (2022) · Frontiers of Computer Science · doi:10.1007/s11704-021-0598-z · Cited by 127 · see citing works
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Survey: federated learning data security and privacy-preserving in edge-Internet of Things
Li, H., Ge, L., & Tian, L. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10774-7 · Cited by 112 · see citing works
“Meanwhile, a fair client selection algorithm design can also prevent malicious clients from participating in training using techniques such as authentication and reputation assessment, reducing the harm of malicious attacks on the model (Mayhoub and M. Shami T, 2023).”
- Authors
- Mayhoub, S., & M. Shami, T.
- Affiliations
- Samara National Research University (RU)
- Corresponding author
- Mayhoub, Samara
- ORCID
- Mayhoub, Samara
- Venue
- Archives of Computational Methods in Engineering
- Published
- 2023-11-01
- Volume
- 31
- Issue
- 2
- Pages
- 1129-1152
- ISSN
- 1134-3060 (ISSN-L)
- 1886-1784
- OpenAlex
- W4388141435
- Cited by
- 47
- References
- 7
- Citations by year
- 2026: 15 · 2025: 25 · 2024: 7
- FWCI
- 4.16
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Archives of Computational Methods in Engineering (publishedVersion)
- 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, client selection, heterogeneous devices, data heterogeneity, privacy-preserving machine learning, edge devices
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