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Machine learning approaches that require data to be copied to a single location are hampered by the challenges of data sharing. Federated Learning (FL) is a promising approach to learn a joint model over all the available data across silos. In many cases, the sites participating in a federation have different data distributions and computational capabilities. In these heterogeneous environments existing approaches exhibit poor performance: synchronous FL protocols are communication efficient, but have slow learning convergence and high energy cost; conversely, asynchronous FL protocols have faster convergence with lower energy cost, but higher communication. In this work, we introduce a novel energy-efficient\n            <jats:italic>Semi-Synchronous Federated Learning<\/jats:italic>\n            protocol that mixes local models periodically with minimal idle time and fast convergence. We show through extensive experiments over established benchmark datasets in the computer-vision domain as well as in real-world biomedical settings that our approach significantly outperforms previous work in\n            <jats:italic>data and computationally heterogeneous environments<\/jats:italic>\n            .\n          <\/jats:p>","DOI":"10.1145\/3524885","type":"journal-article","created":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T15:36:39Z","timestamp":1650641799000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":38,"title":["Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settings"],"prefix":"10.1145","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3343-8335","authenticated-orcid":false,"given":"Dimitris","family":"Stripelis","sequence":"first","affiliation":[{"name":"Information Sciences Institute, University of Southern California, Marina Del Rey, CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4720-8867","authenticated-orcid":false,"given":"Paul M.","family":"Thompson","sequence":"additional","affiliation":[{"name":"Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina Del Rey, CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0087-080X","authenticated-orcid":false,"given":"Jos\u00e9 Luis","family":"Ambite","sequence":"additional","affiliation":[{"name":"Information Sciences Institute, University of Southern California, Marina Del Rey, CA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,6,21]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"265","volume-title":"Proceedings of the 12th  \\( USENIX \\)  Symposium on Operating Systems Design and Implementation","year":"2016","unstructured":"Martin Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek Gordon Murray, Benoit Steiner, Paul A. 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