Byzantine detection for federated learning under highly non-IID data and majority corruptions
Author:
Funder
National Natural Science Foundation of China
Natural Science Foundation of Ningbo
Zhejiang Office of Philosophy and Social Science
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11276-024-03799-x.pdf
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3. Jiang, H., Li, J., Zhao, P., Zeng, F., Xiao, Z., & Iyengar, A. (2021). Location privacy-preserving mechanisms in location-based services: A comprehensive survey. ACM Computing Surveys, 54(1), 4–1436. https://doi.org/10.1145/3423165
4. Wang, H., Kaplan, Z., Niu, D., & Li, B. (2020). Optimizing federated learning on non-iid data with reinforcement learning. In Proceedings of IEEE conference on computer communications.
5. Wang, Z., Song, M., Zhang, Z., Song, Y., Wang, Q., & Qi, H. (2019). Beyond inferring class representatives: User-level privacy leakage from federated learning. In Proceedings of IEEE conference on computer communications.
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