Application of vertical federated learning based on SecureBoost in electric power scene

Ziting Gao; Chenyang Yan; Tingting Yan

doi:10.1117/12.3014768

4 March 2024 Application of vertical federated learning based on SecureBoost in electric power scene

Ziting Gao, Chenyang Yan, Tingting Yan

Proceedings Volume 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023); 129815S (2024) https://doi.org/10.1117/12.3014768
Event: 9th International Symposium on Sensors, Mechatronics, and Automation (ISSMAS 2023), 2023, Nanjing, China

Abstract

Electric data can accurately reflect the operation of the national economy, and can be combined with people's livelihood, environmental protection, economic and other data to create greater social and economic value. However, for the sake of privacy and security, each electric data owner is not willing to share their own data, which makes it impossible to train a more accurate model. In order to break this data island problem, this paper uses vertical federated learning to mine the value of power data and promote data circulation, under the premise of security and compliance, and has achieved good experimental results.

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

Citation Download Citation

Ziting Gao, Chenyang Yan, and Tingting Yan "Application of vertical federated learning based on SecureBoost in electric power scene", Proc. SPIE 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023), 129815S (4 March 2024); https://doi.org/10.1117/12.3014768

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