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Knowl. Discov. Data"],"published-print":{"date-parts":[[2023,8,31]]},"abstract":"<jats:p>\n            Personalized federated learning (PFL) has emerged as a paradigm to provide a personalized model that can fit the local data distribution of each client. One natural choice for PFL is to leverage the fast adaptation capability of meta-learning, where it first obtains a single global model, and each client achieves a personalized model by fine-tuning the global one with its local data. However, existing meta-learning-based approaches implicitly assume that the data distribution among different clients is similar, which may not be applicable due to the property of data heterogeneity in federated learning. In this work, we propose a Group-based Federated Meta-Learning framework, called\n            <jats:italic>G-FML<\/jats:italic>\n            , which adaptively divides the clients into groups based on the similarity of their data distribution, and the personalized models are obtained with meta-learning within each group. In particular, we develop a simple yet effective grouping mechanism to adaptively partition the clients into multiple groups. Our mechanism ensures that each group is formed by the clients with similar data distribution such that the group-wise meta-model can achieve \u201cpersonalization\u201d at large. By doing so, our framework can be generalized to a highly heterogeneous environment. We evaluate the effectiveness of our proposed\n            <jats:italic>G-FML<\/jats:italic>\n            framework on three heterogeneous benchmarking datasets. The experimental results show that our framework improves the model accuracy by up to 13.15% relative to the state-of-the-art federated meta-learning.\n          <\/jats:p>","DOI":"10.1145\/3558005","type":"journal-article","created":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T11:12:04Z","timestamp":1661253124000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":112,"title":["Personalized Federated Learning on Non-IID Data via Group-based Meta-learning"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8732-3675","authenticated-orcid":false,"given":"Lei","family":"Yang","sequence":"first","affiliation":[{"name":"South China University of Technology, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9752-8607","authenticated-orcid":false,"given":"Jiaming","family":"Huang","sequence":"additional","affiliation":[{"name":"South China University of Technology, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7328-8039","authenticated-orcid":false,"given":"Wanyu","family":"Lin","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2725-2529","authenticated-orcid":false,"given":"Jiannong","family":"Cao","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, China"}]}],"member":"320","published-online":{"date-parts":[[2023,3,22]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"1273","volume-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics","author":"McMahan Brendan","year":"2017","unstructured":"Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Ag\u00fcera y Arcas. 2017. 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