Danya Xu , Mingwei Jia , Tao Chen , Yi Liu , Tianyou Chai , Tao Yang
{"title":"利用集群对齐的分散式联合领域泛化进行故障诊断","authors":"Danya Xu , Mingwei Jia , Tao Chen , Yi Liu , Tianyou Chai , Tao Yang","doi":"10.1016/j.conengprac.2024.105951","DOIUrl":null,"url":null,"abstract":"<div><p>Fault diagnosis is important for maintaining safety in industrial scenarios. Due to the complex operating conditions, there is usually a domain shift between training (source) data and testing (target) data. Recent years have witnessed the emergence of numerous transfer learning methods dealing with the domain shift. However, existing methods often fail to deal with the situation where target data are unavailable. Moreover, the majority of these methods require aggregating data distributed across various users (nodes) for model training, raising privacy concerns. Despite existing federated learning methods can protect privacy, they mostly rely on a central server, which may lead to a single point of failure. To address the above issues, we propose a fully decentralized Federated Domain Generalization with Cluster Alignment (FDG-CA), which deals with the domain shift problem without accessing target data and eliminates the need of a central server while protecting privacy. During the training phase, the proposed FDG-CA learns domain-invariant representations by aligning clusters statistics of different source nodes through information exchange. Subsequently, during the testing phase, we propose an ensemble strategy based on a learner filter and a voting scheme to get the prediction results. Experiments demonstrate that our proposed method is superior to existing methods, achieving higher accuracy while addressing privacy concerns.</p></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Decentralized federated domain generalization with cluster alignment for fault diagnosis\",\"authors\":\"Danya Xu , Mingwei Jia , Tao Chen , Yi Liu , Tianyou Chai , Tao Yang\",\"doi\":\"10.1016/j.conengprac.2024.105951\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Fault diagnosis is important for maintaining safety in industrial scenarios. Due to the complex operating conditions, there is usually a domain shift between training (source) data and testing (target) data. Recent years have witnessed the emergence of numerous transfer learning methods dealing with the domain shift. However, existing methods often fail to deal with the situation where target data are unavailable. Moreover, the majority of these methods require aggregating data distributed across various users (nodes) for model training, raising privacy concerns. Despite existing federated learning methods can protect privacy, they mostly rely on a central server, which may lead to a single point of failure. To address the above issues, we propose a fully decentralized Federated Domain Generalization with Cluster Alignment (FDG-CA), which deals with the domain shift problem without accessing target data and eliminates the need of a central server while protecting privacy. During the training phase, the proposed FDG-CA learns domain-invariant representations by aligning clusters statistics of different source nodes through information exchange. Subsequently, during the testing phase, we propose an ensemble strategy based on a learner filter and a voting scheme to get the prediction results. Experiments demonstrate that our proposed method is superior to existing methods, achieving higher accuracy while addressing privacy concerns.</p></div>\",\"PeriodicalId\":50615,\"journal\":{\"name\":\"Control Engineering Practice\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2024-04-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Control Engineering Practice\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0967066124001114\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Control Engineering Practice","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0967066124001114","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Decentralized federated domain generalization with cluster alignment for fault diagnosis
Fault diagnosis is important for maintaining safety in industrial scenarios. Due to the complex operating conditions, there is usually a domain shift between training (source) data and testing (target) data. Recent years have witnessed the emergence of numerous transfer learning methods dealing with the domain shift. However, existing methods often fail to deal with the situation where target data are unavailable. Moreover, the majority of these methods require aggregating data distributed across various users (nodes) for model training, raising privacy concerns. Despite existing federated learning methods can protect privacy, they mostly rely on a central server, which may lead to a single point of failure. To address the above issues, we propose a fully decentralized Federated Domain Generalization with Cluster Alignment (FDG-CA), which deals with the domain shift problem without accessing target data and eliminates the need of a central server while protecting privacy. During the training phase, the proposed FDG-CA learns domain-invariant representations by aligning clusters statistics of different source nodes through information exchange. Subsequently, during the testing phase, we propose an ensemble strategy based on a learner filter and a voting scheme to get the prediction results. Experiments demonstrate that our proposed method is superior to existing methods, achieving higher accuracy while addressing privacy concerns.
期刊介绍:
Control Engineering Practice strives to meet the needs of industrial practitioners and industrially related academics and researchers. It publishes papers which illustrate the direct application of control theory and its supporting tools in all possible areas of automation. As a result, the journal only contains papers which can be considered to have made significant contributions to the application of advanced control techniques. It is normally expected that practical results should be included, but where simulation only studies are available, it is necessary to demonstrate that the simulation model is representative of a genuine application. Strictly theoretical papers will find a more appropriate home in Control Engineering Practice''s sister publication, Automatica. It is also expected that papers are innovative with respect to the state of the art and are sufficiently detailed for a reader to be able to duplicate the main results of the paper (supplementary material, including datasets, tables, code and any relevant interactive material can be made available and downloaded from the website). The benefits of the presented methods must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods. Moreover, a thorough analysis of failures that may happen in the design process and implementation can also be part of the paper.
The scope of Control Engineering Practice matches the activities of IFAC.
Papers demonstrating the contribution of automation and control in improving the performance, quality, productivity, sustainability, resource and energy efficiency, and the manageability of systems and processes for the benefit of mankind and are relevant to industrial practitioners are most welcome.