has been cited by the following article(s):
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[1]
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A Theoretical Comparison of Federated Learning with Differential Privacy and Blockchain for Security and Privacy in IoMT
2025 19th International Conference on Ubiquitous Information Management and Communication (IMCOM),
2025
DOI:10.1109/IMCOM64595.2025.10857505
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[2]
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Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontiers
ACM Transactions on Intelligent Systems and Technology,
2024
DOI:10.1145/3678182
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[3]
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A survey on federated learning: a perspective from multi-party computation
Frontiers of Computer Science,
2024
DOI:10.1007/s11704-023-3282-7
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[4]
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Federated learning with hybrid differential privacy for secure and reliable cross‐IoT platform knowledge sharing
SECURITY AND PRIVACY,
2024
DOI:10.1002/spy2.374
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[5]
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Differential Privacy Based Federated Learning Techniques in IoMT: A Review
2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM),
2024
DOI:10.1109/IMCOM60618.2024.10418361
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[6]
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Federated learning with hybrid differential privacy for secure and reliable cross‐IoT platform knowledge sharing
SECURITY AND PRIVACY,
2024
DOI:10.1002/spy2.374
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[7]
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Federated learning with hybrid differential privacy for secure and reliable cross‐IoT platform knowledge sharing
SECURITY AND PRIVACY,
2024
DOI:10.1002/spy2.374
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[8]
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Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontiers
ACM Transactions on Intelligent Systems and Technology,
2024
DOI:10.1145/3678182
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