Abstract
Federated learning has received extensive attention in recent years since the clients only need to share their local gradients with the servers without directly sharing their datasets to train the model. However, the existing research shows that the attackers can still reconstruct private information from shared gradients, resulting in privacy leakage. In addition, the aggregated results could be tampered with by servers or attackers. In this paper, we propose a secure and verifiable federated learning training scheme (SVFLS) to protect the privacy of data owners and verify aggregated results. Specifically, we employ threshold paillier encryption to protect the local gradients of data owners and use the bilinear aggregate signature to verify the correctness (or integrity) of aggregated results. Furthermore, our scheme can tolerate data owners dropping out during the training phase. We conduct extensive experiments on real datasets and demonstrate that our scheme is effective and practical.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China under grants 62072134 and U2001205, and the Key projects of Guangxi Natural Science Foundation under grant 2019JJD170020, and the Key Research and Development Program of Hubei Province under Grant 2021BEA163.
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Liu, Y., Hu, G., Zhang, Y., Zhang, M. (2022). SVFLS: A Secure and Verifiable Federated Learning Training Scheme. In: Ahene, E., Li, F. (eds) Frontiers in Cyber Security. FCS 2022. Communications in Computer and Information Science, vol 1726. Springer, Singapore. https://doi.org/10.1007/978-981-19-8445-7_9
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