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Although federated transfer learning (FTL) provides decentralized solutions, existing methods do not adequately address the poor classification results caused by data imbalance within the client. This study integrates optimized oversampling techniques into a federated transfer learning framework, proposes an optimized oversampling-based federated transfer learning approach. Firstly, annular region sample optimization (ARSO) is proposed to tackle ambiguous class boundaries from arbitrary sample selection in synthetic minority oversampling technique (SMOTE) by optimizing the sample selection strategy through annular regions. Then ARSO is integrated into a federated transfer learning framework with a One-Dimensional Convolutional Neural Network (1D-CNN), balance the amount of data among clients by extending a few classes of data before federated transfer learning, screen high-quality source clients for knowledge transfer based on a privacy-preserving transfer mechanism selects source clients via category-completeness metadata, and aligns domains using encrypted feature embeddings, proposed the annular region sample optimization federated transfer learning (ARSO-FTL). Experiments demonstrate ARSO-FTL achieves leading performance, recording 96.65% accuracy and an AUC of 0.96. It outperforms distributed baselines and effectively addresses intra-client imbalance and Non-IID challenges within federated transfer learning.<\/jats:p>","DOI":"10.1093\/jcde\/qwaf068","type":"journal-article","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T14:28:02Z","timestamp":1753280882000},"page":"154-172","source":"Crossref","is-referenced-by-count":4,"title":["An optimized oversampling-based federated transfer learning approach for rotating machinery cluster fault diagnosis"],"prefix":"10.1093","volume":"12","author":[{"given":"Zhao","family":"Xu","sequence":"first","affiliation":[{"name":"Guizhou University School of Mechanical Engineering, , Guiyang 55000 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5212-8546","authenticated-orcid":false,"given":"Liya","family":"Yu","sequence":"additional","affiliation":[{"name":"Guizhou University School of Mechanical Engineering, , Guiyang 55000 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