Client Evaluation and Revision in Federated Learning: Towards Defending Free-Riders and Promoting Fairness

Author:

He Jiatong,Wu Libing,Zhang Zhuangzhuang,Lu Na,Wei Xuejiang

Publisher

Springer Nature Singapore

Reference27 articles.

1. Cao, X., Fang, M., Liu, J., Gong, N.Z.: FLTrust: byzantine-robust federated learning via trust bootstrapping. In: 28th Annual Network and Distributed System Security Symposium (NDSS) (2021)

2. Chaddad, A., Wu, Y., Desrosiers, C.: Federated learning for healthcare applications. IEEE Internet Things J. 11(5), 7339–7358 (2024). https://doi.org/10.1109/JIOT.2023.3325822

3. Chen, B., Wu, L., Kumar, N., Choo, K.R., He, D.: Lightweight searchable public-key encryption with forward privacy over iiot outsourced data. IEEE Trans. Emerg. Top. Comput. 9(4), 1753–1764 (2021)

4. Diao, E., Ding, J., Tarokh, V.: HeteroFL: computation and communication efficient federated learning for heterogeneous clients. In: 9th International Conference on Learning Representations (ICLR) (2021)

5. Fang, M., Cao, X., Jia, J., Gong, N.: Local model poisoning attacks to byzantine-robust federated learning. In: 29th USENIX Security Symposium (USENIX Security 2020), pp. 1605–1622 (2020)

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