Abstract
Federated learning is a novel machine learning framework that effectively satisfies the requirements of multiple organizations for data usage and model training while meeting privacy protection, data security, and government regulations. However, recent research has shown that attackers can infer users’ private information from their shared model parameters. To address the issue, in this paper, we propose the smart contract assisted secure aggregation scheme (SCSA). Firstly, we present a triple layers architecture based on blockchain for secure aggregation, which can adapt to application scenarios where a large amount of devices are involved in model training. Then, with the help of smart contracts, our scheme can efficiently distribute security masks to users in a decentralized form to ensure the security of parameters, and combine with secret sharing to design a double fault tolerance mechanism to effectively improve the robustness of the system. Finally, the theoretical analysis and simulation experiments prove that our scheme has high security and robustness while maintaining efficiency.
Supported by the National Natural Science Foundation of China under grant 62072065.
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Wang, B., Hu, C., Liu, Z. (2022). A Secure Aggregation Scheme for Model Update in Federated Learning. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13471. Springer, Cham. https://doi.org/10.1007/978-3-031-19208-1_41
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