A federated learning scheme based on personalized differential privacy and secret sharing

Zhiqing Wu; Baocheng Wang

doi:10.1117/12.3026504

27 March 2024 A federated learning scheme based on personalized differential privacy and secret sharing

Zhiqing Wu, Baocheng Wang

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 1310549 (2024) https://doi.org/10.1117/12.3026504
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

In traditional federated learning, participants train models by sharing model data. However, most existing federated learning frameworks are based on a uniform privacy budget, which cannot meet personalized privacy requirements. Moreover, during the model aggregation process, few federated learning frameworks consider potential malicious attacks or data leakage risks among participants. To address these issues, this paper proposes a federated learning scheme based on personalized differential privacy and secret sharing (PDPSS-FL). This scheme provides personalized differential privacy protection for participants, adding personalized noise to the model to preserve their privacy. Secret sharing techniques are employed during model updates and parameter transmissions to ensure secure model updates in the presence of honest-but-curious servers. Experimental results demonstrate that the proposed scheme generates high-quality models while satisfying personalized privacy protection requirements in a secure environment.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhiqing Wu and Baocheng Wang "A federated learning scheme based on personalized differential privacy and secret sharing", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 1310549 (27 March 2024); https://doi.org/10.1117/12.3026504

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