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{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:52:09Z","timestamp":1740149529766,"version":"3.37.3"},"reference-count":43,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,24]],"date-time":"2022-09-24T00:00:00Z","timestamp":1663977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"EPSRC","award":["EP\/T021985\/1","EP\/W003325\/1","EP\/V042017\/1"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Federated Learning (FL) enables multiple clients to train a shared model collaboratively without sharing any personal data. However, selecting a model and adapting it quickly to meet user expectations in a large-scale FL application with heterogeneous devices is challenging. In this paper, we propose a model selection and adaptation system for Federated Learning (FedMSA), which includes a hardware-aware model selection algorithm that trades-off model training efficiency and model performance base on FL developers\u2019 expectation. Meanwhile, considering the expected model should be achieved by dynamic model adaptation, FedMSA supports full automation in building and deployment of the FL task to different hardware at scale. Experiments on benchmark and real-world datasets demonstrate the effectiveness of the model selection algorithm of FedMSA in real devices (e.g., Raspberry Pi and Jetson nano).<\/jats:p>","DOI":"10.3390\/s22197244","type":"journal-article","created":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T07:34:17Z","timestamp":1664177657000},"page":"7244","source":"Crossref","is-referenced-by-count":5,"title":["FedMSA: A Model Selection and Adaptation System for Federated Learning"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1964-3682","authenticated-orcid":false,"given":"Rui","family":"Sun","sequence":"first","affiliation":[{"name":"School of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"}]},{"given":"Yinhao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"}]},{"given":"Tejal","family":"Shah","sequence":"additional","affiliation":[{"name":"School of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"}]},{"given":"Ringo W. H.","family":"Sham","sequence":"additional","affiliation":[{"name":"School of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4101-2115","authenticated-orcid":false,"given":"Tomasz","family":"Szydlo","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, AGH University of Science and Technology, 30-059 Krakow, Poland"}]},{"given":"Bin","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"}]},{"given":"Dhaval","family":"Thakker","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Bradford, Bradford BD7 1DP, UK"}]},{"given":"Rajiv","family":"Ranjan","sequence":"additional","affiliation":[{"name":"School of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1145\/1721654.1721672","article-title":"A view of cloud computing","volume":"53","author":"Armbrust","year":"2010","journal-title":"Commun. 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