An efficient and privacy-preserving federal learning scheme

Shaohua Liu; Zhiqiang Fu

doi:10.1117/12.3011282

8 November 2023 An efficient and privacy-preserving federal learning scheme

Shaohua Liu, Zhiqiang Fu

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 129230C (2023) https://doi.org/10.1117/12.3011282
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

Abstract

With the rapid development of artificial intelligence technologies, federated learning (FL) is increasingly linked to the Internet of Things (IoT). Recently, researchers have found that the screening and privacy of low-quality users has a significant impact on the training of federation learning. The goal of this work is to ensure that data is trained locally, that data is not out of domain, and that the process of model aggregation is performed in ciphertext, while FL models can be generated quickly and iteratively. However, existing work is still in its infancy, with most research efforts focused on the protection of private data, and implementing their solutions is the main challenge .To address this problem, we propose an efficient and privacy-preserving federal learning scheme with the ability to screen users with low computational power. Specifically, we design a new scheme to reduce the impact of excessive time costs caused by low computational power users by iteratively executing our "Low quality user screening algorithm". Meanwhile, to achieve secure aggregation of the model, we invoke a security framework based on the threshold Palile cryptosystem [8]. Experimentally, we prove that our scheme can greatly improve the training efficiency and save the time overhead of training.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shaohua Liu and Zhiqiang Fu "An efficient and privacy-preserving federal learning scheme", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 129230C (8 November 2023); https://doi.org/10.1117/12.3011282

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