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Netw."],"published-print":{"date-parts":[[2024,5,31]]},"abstract":"<jats:p>\n            The Internet of Things (IoT) has revolutionized the connectivity of diverse sensing devices, generating an enormous volume of data. However, applying machine learning algorithms to sensing devices presents substantial challenges due to resource constraints and privacy concerns. Federated learning (FL) emerges as a promising solution allowing for training models in a distributed manner while preserving data privacy on client devices. We contribute\n            <jats:italic>SAFI<\/jats:italic>\n            , a semi-asynchronous FL approach based on clustering to achieve a novel in-cluster synchronous and out-cluster asynchronous FL training mode. Specifically, we propose a three-tier architecture to enable IoT data processing on edge devices and design a clustering selection module to effectively group heterogeneous edge devices based on their processing capacities. The performance of\n            <jats:italic>SAFI<\/jats:italic>\n            has been extensively evaluated through experiments conducted on a real-world testbed. As the heterogeneity of edge devices increases,\n            <jats:italic>SAFI<\/jats:italic>\n            surpasses the baselines in terms of the convergence time, achieving a speedup of approximately \u00d7 3 when the heterogeneity ratio is 7:1. Moreover,\n            <jats:italic>SAFI<\/jats:italic>\n            demonstrates favorable performance in non-independent and identically distributed settings and requires lower communication cost compared to FedAsync. Notably,\n            <jats:italic>SAFI<\/jats:italic>\n            is the first Java-implemented FL approach and holds significant promise to serve as an efficient FL algorithm in IoT environments.\n          <\/jats:p>","DOI":"10.1145\/3639825","type":"journal-article","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T12:41:27Z","timestamp":1706186487000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Behave Differently when Clustering: A Semi-asynchronous Federated Learning Approach for IoT"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0790-6035","authenticated-orcid":false,"given":"Boyu","family":"Fan","sequence":"first","affiliation":[{"name":"University of Helsinki, Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5945-9551","authenticated-orcid":false,"given":"Xiang","family":"Su","sequence":"additional","affiliation":[{"name":"Norwegian University of Science and Technolog, Gj\u00f8vik, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4220-3650","authenticated-orcid":false,"given":"Sasu","family":"Tarkoma","sequence":"additional","affiliation":[{"name":"University of Helsinki, Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6026-1083","authenticated-orcid":false,"given":"Pan","family":"Hui","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Helsinki, Hong Kong and University of Helsinki, Kowloon, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,2,23]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3030072"},{"key":"e_1_3_1_3_2","first-page":"437","volume-title":"Proceedings of the 21th International European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning","author":"Anguita Davide","year":"2013","unstructured":"Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra Perez, and Jorge Luis Reyes Ortiz. 2013. 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