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Secure aggregation of power information resources based on improved transfer learning

Ruijin He et al.

International Journal of Information Technology and Management2025https://doi.org/10.1504/ijitm.2025.151549article
AJG 1
Weight
0.50

What the paper says

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.

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https://doi.org/https://doi.org/10.1504/ijitm.2025.151549

Or copy a formatted citation

@article{ruijin2025,
  title        = {{Secure aggregation of power information resources based on improved transfer learning}},
  author       = {Ruijin He et al.},
  journal      = {International Journal of Information Technology and Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijitm.2025.151549},
}

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Secure aggregation of power information resources based on improved transfer learning

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.