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Federated learning, a recent development in machine learning methods, has gained traction in privacy-sensitive tasks, such as personal voice assistants in home environments. However, its application in heterogeneous multi-domain scenarios for enhancing system customization remains underexplored. In this paper, we propose the utilization of federated learning in heterogeneous situations to enable adaptation across multiple domains. We also introduce a personalized federated learning algorithm designed to effectively leverage limited domain data, resulting in improved learning outcomes. Furthermore, we present a strategy for implementing the federated learning algorithm in practical, real-world continual learning scenarios, demonstrating promising results. The proposed federated learning method exhibits superior performance across a range of synthesized complex conditions and continual learning settings, compared to conventional training methods.<\/jats:p>","DOI":"10.1186\/s13636-023-00299-2","type":"journal-article","created":{"date-parts":[[2023,9,5]],"date-time":"2023-09-05T11:02:24Z","timestamp":1693911744000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Learning domain-heterogeneous speaker recognition systems with personalized continual federated learning"],"prefix":"10.1186","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9629-6111","authenticated-orcid":false,"given":"Zhiyong","family":"Chen","sequence":"first","affiliation":[]},{"given":"Shugong","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,5]]},"reference":[{"key":"299_CR1","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.neunet.2021.03.004","volume":"140","author":"Z Bai","year":"2021","unstructured":"Z. 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