FL4C^2: a trustworthy intelligent cloud computing for private machine learning

He Yang; Jian An; Xin He; Yuhao Shen

doi:10.1117/12.2691899

23 August 2023 FL4C^2: a trustworthy intelligent cloud computing for private machine learning

He Yang, Jian An, Xin He, Yuhao Shen

Proceedings Volume 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023); 127843G (2023) https://doi.org/10.1117/12.2691899
Event: 2023 2nd International Conference on Applied Statistics, Computational Mathematics and Software Engineering (ASCMSE 2023), 2023, Kaifeng, China

Abstract

The cloud operating system plays a crucial role in supporting machine learning tasks by offering powerful computing capabilities and abundant resources. This enables the implementation of various complex applications, including health monitoring and noise pollution assessment. However, this paradigm involves centralized data storage and processing on a central server, which raises significant data security concerns. To tackle these security issues, we propose a state-ofthe- art framework called FL4C^2, which amalgamates federated learning (FL) and cloud computing (CC) eliminating their inherent drawbacks. Specifically, FL4C^2 allows users to collaboratively learn a shared model by solely submitting model parameters to the server, which ensures that user data remains localized and private, thereby preventing any potential data leakage. Moreover, FL4C^2 incorporates an encryption parameter transmission mechanism, which safeguards against indirect data leakage resulting from sharing the model parameters. Simultaneously, we employ an anonymity strategy to generate verifiable anonymous identities, which enhances identity privacy protection. Extensive experimental results based on the KunPeng operating system demonstrate that FL4C^2 achieves superior performance while effectively preserving user privacy.

Citation Download Citation

He Yang, Jian An, Xin He, and Yuhao Shen "FL4C^2: a trustworthy intelligent cloud computing for private machine learning", Proc. SPIE 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023), 127843G (23 August 2023); https://doi.org/10.1117/12.2691899

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KEYWORDS

Machine learning

Data modeling

Windows

Clouds

Data privacy

Education and training

Cloud computing

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