Abstract
In this study, we introduce a novel Hybrid Federated Learning (HybridFL) approach aimed at enhancing privacy and accuracy in collaborative machine learning scenarios. Our methodology integrates Differential Privacy (DP) and secret sharing techniques to address inference risks during training and protect against information leakage in the output model. Drawing inspiration from recent advances, we present a HybridFL framework that combines the strengths of Homomorphic Encryption (HE) and Multi-Party Computation (MPC) to achieve secure computation without the computational overhead of pure HE methods. Our contributions include a privacy-preserving design for Federated Learning (FL) that ensures local data privacy through secret sharing while leveraging DP mechanisms for noise addition. The system offers resilience against unreliable participants and is evaluated using various machine learning models, including Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), and linear regression. Furthermore, we address potential external threats by deploying predictive model outputs as robust services against inference attacks. Experimental results demonstrate improved accuracy and convergence speed, establishing the viability of HybridFL as an effective solution for collaborative machine learning with enhanced privacy guarantees.
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Acknowledgment
This work is supported by the National Natural Science Foundation of China (62472252, 62172258), TaiShan Scholars Program (tsqn202211280), Shandong Provincial Natural Science Foundation (ZR2024QF131, ZR2023LZH014, ZR2022ZD01, ZR2022MF264, ZR2021LZH007), Shandong Provincial Key R&D Program of China (2021SFGC0401, 2021CXGC010103), Department of Science & Technology of Shandong Province (SYS202201), and Quan Cheng Laboratory (QCLZD202302).
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Ali, S., Mamoon, S., Usman, A., Abidin, Z.u., Zhao, C. (2025). HybridFL: Hybrid Approach Toward Privacy-Preserving Federated Learning. In: Cai, J., Zhou, Z., Chen, K. (eds) Security and Privacy in New Computing Environments. SPNCE 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 525. Springer, Cham. https://doi.org/10.1007/978-3-031-73699-5_1
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