Jiale Han
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Jiale Han has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Wei Zhou
College of Mathematics and Computer, Jilin Normal University, Siping 136000, China
1 shared publication - Xuesong Zhang
Jilin Engineering Vocational College
1 shared publication - Zhenjiang Tan
College of Mathematics and Computer, Jilin Normal University, Siping 136000, China
1 shared publication - Zhiqiang Xie
School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
1 shared publication
Latest Publications
- Open Access
- 29 December 2024
- Published by MDPI AG in Electronics
- Vol. 14 (1) , 92
Federated Learning-Based Equipment Fault-Detection Algorithm
To address the issue of imbalanced distribution in equipment fault data, this paper proposes an improved FedAvg aggregation algorithm. By dynamically adjusting aggregation weights, it ensures the adaptability of fault data weights across different distribution dimensions during the aggregation process. The algorithm first introduces a contribution mechanism and then combines the contribution degree of each client with the weight of their dataset for model aggregation, resulting in the FedAvg-ContribData algorithm. Experimental data demonstrate that, compared to FedAvg and FedAvg-Data algorithms, our proposed algorithm improves accuracy by 39.7% and 7.9%, precision by 40.4% and 9.1%, recall rate by 41.0% and 7.8%, and F1 score by 46.6% and 9.3%.
- 29 December 2024
- Published by MDPI AG in Electronics
- Vol. 14 (1) , 92
Open AccessFederated Learning-Based Equipment Fault-Detection Algorithm
To address the issue of imbalanced distribution in equipment fault data, this paper proposes an improved FedAvg aggregation algorithm. By dynamically adjusting aggregation weights, it ensures the adaptability of fault data weights across different distribution dimensions during the aggregation process. The algorithm first introduces a contribution mechanism and then combines the contribution degree of each client with the weight of their dataset for model aggregation, resulting in the FedAvg-ContribData algorithm. Experimental data demonstrate that, compared to FedAvg and FedAvg-Data algorithms, our proposed algorithm improves accuracy by 39.7% and 7.9%, precision by 40.4% and 9.1%, recall rate by 41.0% and 7.8%, and F1 score by 46.6% and 9.3%.