Privacy efficient federal learning approach for smart security

Runjia Zhan; Chen Fang; Zhitong Zhu

doi:10.1117/12.3029058

22 May 2024 Privacy efficient federal learning approach for smart security

Runjia Zhan, Chen Fang, Zhitong Zhu

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131760I (2024) https://doi.org/10.1117/12.3029058
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

In the traditional security field, user data is collected by different monitoring points, which can not be shared effectively, resulting in the phenomenon of "data island". To this end, this paper designs a privacy-efficient federated learning method for the field of intelligent security. By using a horizontal federated learning framework, under the premise of ensuring that the data does not go out of each region, the distributed monitoring points across the domain are jointly trained to ensure data privacy and security. In order to ensure the security of gradient data transmission in the process of joint modeling, a privacy protection protocol based on additive secret sharing was designed to effectively prevent gradient leakage attacks and enhance the efficiency of calculation. This paper proposes a communication compression strategy based on adaptive bidirectional TOP-K gradient sparsification, which greatly reduces the communication overhead in the joint modeling process while ensuring the model training performance. Our experiments on COCO dataset and Gunknipe-dataset dataset show that our method can not only compress communication overhead and improve training efficiency, but also effectively prevent gradient leakage attacks and ensure the security of transmitted data.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Runjia Zhan, Chen Fang, and Zhitong Zhu "Privacy efficient federal learning approach for smart security", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131760I (22 May 2024); https://doi.org/10.1117/12.3029058

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