Abstract
In Internet of Things (IoT), it is often impossible to share datasets owned by different participants (usually IoT devices) for machine learning model training due to privacy concerns. Federated learning (FL) is a promising technique to address this challenge. However, existing FL schemes face the problem of how to avoid low-quality/malicious update. To solve this problem, we propose a privacy-preserving and reliable federated learning scheme (PPRFLS) to select reliable participants and evaluate the quality of the participants’ updates. Analysis shows that the proposed scheme achieves data privacy and model reliability.
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Notes
- 1.
The elbow method [5] can be used to select the value m.
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Acknowledgement
This work is supported by the NSF of China under Grants 61972159, 61572198; by the Open Research Fund of Engineering Research Center of Software/Hardware Co-design Technology and Application, Ministry of Education (East China Normal University); by Guangxi Key Laboratory of Cryptography and Information Security (No. GCIS202109).
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Lu, Y., Zhang, L., Wang, L., Gao, Y. (2022). Privacy-Preserving and Reliable Federated Learning. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13157. Springer, Cham. https://doi.org/10.1007/978-3-030-95391-1_22
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