A Federated Adversarial Fault Diagnosis Method Driven by Fault Information Discrepancy
Federated learning (FL) facilitates the collaborative optimization of fault diagnosis models across multiple clients. However, the performance of the global model in the federated center is contingent upon the effectiveness of the local models. Low-quality local models participating in the federatio...
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MDPI AG
2024-08-01
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| Online Access: | https://www.mdpi.com/1099-4300/26/9/718 |
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| author | Jiechen Sun Funa Zhou Jie Chen Chaoge Wang Xiong Hu Tianzhen Wang |
| author_facet | Jiechen Sun Funa Zhou Jie Chen Chaoge Wang Xiong Hu Tianzhen Wang |
| author_sort | Jiechen Sun |
| collection | DOAJ |
| description | Federated learning (FL) facilitates the collaborative optimization of fault diagnosis models across multiple clients. However, the performance of the global model in the federated center is contingent upon the effectiveness of the local models. Low-quality local models participating in the federation can result in negative transfer within the FL framework. Traditional regularization-based FL methods can partially mitigate the performance disparity between local models. Nevertheless, they do not adequately address the inconsistency in model optimization directions caused by variations in fault information distribution under different working conditions, thereby diminishing the applicability of the global model. This paper proposes a federated adversarial fault diagnosis method driven by fault information discrepancy (FedAdv_ID) to address the challenge of constructing an optimal global model under multiple working conditions. A consistency evaluation metric is introduced to quantify the discrepancy between local and global average fault information, guiding the federated adversarial training mechanism between clients and the federated center to minimize feature discrepancy across clients. In addition, an optimal aggregation strategy is developed based on the information discrepancies among different clients, which adaptively learns the aggregation weights and model parameters needed to reduce global feature discrepancy, ultimately yielding an optimal global model. Experiments conducted on benchmark and real-world motor-bearing datasets demonstrate that FedAdv_ID achieves a fault diagnosis accuracy of 93.09% under various motor operating conditions, outperforming model regularization-based FL methods by 17.89%. |
| format | Article |
| id | doaj-art-1da54bdc00554397b5c3ea6138b15cb4 |
| institution | OA Journals |
| issn | 1099-4300 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-1da54bdc00554397b5c3ea6138b15cb42025-08-20T01:55:27ZengMDPI AGEntropy1099-43002024-08-0126971810.3390/e26090718A Federated Adversarial Fault Diagnosis Method Driven by Fault Information DiscrepancyJiechen Sun0Funa Zhou1Jie Chen2Chaoge Wang3Xiong Hu4Tianzhen Wang5School of Logistic Engineering, Shanghai Maritime University, Shanghai 201306, ChinaSchool of Logistic Engineering, Shanghai Maritime University, Shanghai 201306, ChinaSchool of Logistic Engineering, Shanghai Maritime University, Shanghai 201306, ChinaSchool of Logistic Engineering, Shanghai Maritime University, Shanghai 201306, ChinaSchool of Logistic Engineering, Shanghai Maritime University, Shanghai 201306, ChinaSchool of Logistic Engineering, Shanghai Maritime University, Shanghai 201306, ChinaFederated learning (FL) facilitates the collaborative optimization of fault diagnosis models across multiple clients. However, the performance of the global model in the federated center is contingent upon the effectiveness of the local models. Low-quality local models participating in the federation can result in negative transfer within the FL framework. Traditional regularization-based FL methods can partially mitigate the performance disparity between local models. Nevertheless, they do not adequately address the inconsistency in model optimization directions caused by variations in fault information distribution under different working conditions, thereby diminishing the applicability of the global model. This paper proposes a federated adversarial fault diagnosis method driven by fault information discrepancy (FedAdv_ID) to address the challenge of constructing an optimal global model under multiple working conditions. A consistency evaluation metric is introduced to quantify the discrepancy between local and global average fault information, guiding the federated adversarial training mechanism between clients and the federated center to minimize feature discrepancy across clients. In addition, an optimal aggregation strategy is developed based on the information discrepancies among different clients, which adaptively learns the aggregation weights and model parameters needed to reduce global feature discrepancy, ultimately yielding an optimal global model. Experiments conducted on benchmark and real-world motor-bearing datasets demonstrate that FedAdv_ID achieves a fault diagnosis accuracy of 93.09% under various motor operating conditions, outperforming model regularization-based FL methods by 17.89%.https://www.mdpi.com/1099-4300/26/9/718fault diagnosisfederated learninginformation discrepancyfederated adversarial |
| spellingShingle | Jiechen Sun Funa Zhou Jie Chen Chaoge Wang Xiong Hu Tianzhen Wang A Federated Adversarial Fault Diagnosis Method Driven by Fault Information Discrepancy Entropy fault diagnosis federated learning information discrepancy federated adversarial |
| title | A Federated Adversarial Fault Diagnosis Method Driven by Fault Information Discrepancy |
| title_full | A Federated Adversarial Fault Diagnosis Method Driven by Fault Information Discrepancy |
| title_fullStr | A Federated Adversarial Fault Diagnosis Method Driven by Fault Information Discrepancy |
| title_full_unstemmed | A Federated Adversarial Fault Diagnosis Method Driven by Fault Information Discrepancy |
| title_short | A Federated Adversarial Fault Diagnosis Method Driven by Fault Information Discrepancy |
| title_sort | federated adversarial fault diagnosis method driven by fault information discrepancy |
| topic | fault diagnosis federated learning information discrepancy federated adversarial |
| url | https://www.mdpi.com/1099-4300/26/9/718 |
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