Abstract
Although supporting training deep learning models distributed without disclosing the raw privacy data, federated learning (FL) is still vulnerable to inference attacks. This paper proposes ComEnc-FL, a privacy-enhancing federated learning system that combats these vulnerabilities. ComEnc-FL uses secure multi-party computation and parameter encoding to reduce communication and computational expenses. ComEnc-FL surpasses typical secure multi-party computation systems in training time and data transfer bandwidth. ComEnc-FL matches the base FL framework and outperforms differential privacy-safe frameworks. We also show that parameter compression reduces encryption time, improving model performance over the FL.
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Tran, A.T., Luong, T.D., Pham, X.S. (2023). A Novel Privacy-Preserving Federated Learning Model Based on Secure Multi-party Computation. In: Honda, K., Le, B., Huynh, VN., Inuiguchi, M., Kohda, Y. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2023. Lecture Notes in Computer Science(), vol 14376. Springer, Cham. https://doi.org/10.1007/978-3-031-46781-3_27
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DOI: https://doi.org/10.1007/978-3-031-46781-3_27
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