FedPV-FS: A Feature Selection Method for Federated Learning in Insurance Precision Marketing

Author:

Wang Chunkai,Feng Jian

Publisher

Springer Nature Switzerland

Reference20 articles.

1. McMahan, H.B., Moore, E., Ramage, D., et al.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282. PMLR (2017)

2. Yang, Q., Liu, Y., Cheng, Y., et al.: Federated learning. Synth. Lect. Artif. Intell. Mach. Learn. 13(3), 1–207 (2019)

3. Wang, J., Zhang, A., Li, X., et al.: Efficient participant contribution evaluation for horizontal and vertical federated learning. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE), pp. 911–923 (2022)

4. Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16–28 (2014)

5. Pan, F., Meng, D., Zhang, Y., et al.: Secure federated feature selection for cross-feature federated learning (2020)

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