Attacks and countermeasures on federated learning via historical knowledge modeling
Abstract Federated learning has emerged as a promising paradigm for privacy-preserving multi-source data fusion. However, its distributed nature makes it vulnerable to poisoning attacks. Malicious clients inject poisoned noises into their local models, severely degrading the global model’s performan...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44443-025-00115-1 |
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