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Federated learning is a promising approach for training machine learning models while preserving data privacy.
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2022
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2020
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2022
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2023
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2023
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X. Lyu, Y. Han, W. Wang, J. Liu, B. Wang, K. Chen, Y. Li, J. Liu, and X. Zhang, “Coba: Collusive backdoor attacks with optimized trigger to federated learning,” IEEE Transactions on Dependable and Secure Computing , 2024
2024
Closest in time.
C. Shi, S. Ji, X. Pan, X. Zhang, M. Zhang, M. Yang, J. Zhou, J. Yin, and T. Wang, “Towards practical backdoor attacks on federated learning systems,” IEEE Transactions on Dependable and Secure Computing , 2024
2024
Closest in time.
R. Xu, B. Li, C. Li, J. Joshi, S. Ma, T. Zhou, J. Dong, and J. Li, “Tapfed: Threshold secure aggregation for privacy-preserving federated learning,” IEEE Transactions on Dependable and Secure Computing , 2024
2024
Closest in time.
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2025
Closest in time.