Title: Secure aggregation of power information resources based on improved transfer learning

Authors: Ruijin He; Jinhua Huang; Huiying Lu; Xuxian Wang; Yuhao Liu; Minghui Li

Addresses: Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou, 510000, China; Key Laboratory of Power Grid Automation of China Southern Power Grid Co., Ltd., Guangzhou, 510000, China ' Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou, 510000, China; Key Laboratory of Power Grid Automation of China Southern Power Grid Co., Ltd., Guangzhou, 510000, China ' Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou, 510000, China; Key Laboratory of Power Grid Automation of China Southern Power Grid Co., Ltd., Guangzhou, 510000, China ' Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou, 510000, China; Key Laboratory of Power Grid Automation of China Southern Power Grid Co., Ltd., Guangzhou, 510000, China ' Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou, 510000, China; Key Laboratory of Power Grid Automation of China Southern Power Grid Co., Ltd., Guangzhou, 510000, China ' Guangdong Power Grid Energy Investment Co., Ltd., 510000, China

Abstract: In order to solve the problems of long time, low accuracy, and low data integrity index of traditional methods for secure aggregation of power information resources, a secure aggregation method of power information resources based on improved transfer learning is proposed. Firstly, select transfer learning models based on the characteristics of power information resources. Secondly, identify key technologies for improving the transfer learning process and strategies for enhancing model adaptability and robustness. Finally, a secure aggregation framework for power information resources is constructed, which utilises data generation, aggregation, and secure sharing to achieve secure aggregation of power information resources. The experimental results show that the resource aggregation time of the proposed method varies within 0.1 s-0.8 s, and the accuracy of power information resource aggregation can reach 90%, with a high data integrity index. This proves that the method has good security aggregation effect on power information resources.

Keywords: improved transfer learning; power information resources; secure aggregation; data generation; security sharing.

DOI: 10.1504/IJITM.2025.151549

International Journal of Information Technology and Management, 2025 Vol.24 No.3/4, pp.259 - 280

Received: 19 Sep 2024
Accepted: 07 Feb 2025
Published online: 05 Feb 2026 *

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