SVFLS: A Secure and Verifiable Federated Learning Training Scheme
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-8445-7_9
Reference24 articles.
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3. Zhao, L., et al.: Shielding collaborative learning: mitigating poisoning attacks through client-side detection. IEEE Trans. Depend. Secure Comput. 18(5), 2029–2041 (2020)
4. Saxena, N., Grijalva, S., Chukwuka, V., Vasilakos, A.V.: Network security and privacy challenges in smart vehicle-to-grid. IEEE Wirel. Commun. 24(4), 88–98 (2017)
5. Mohassel, P., Zhang, Y.: SecureML: a system for scalable privacy-preserving machine learning. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 19–38. IEEE (2017)
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