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Standard FL infrastructures are however designed to have no energy or performance impact on mobile devices, and are therefore not suitable for applications that require frequent (\n            <jats:italic>online<\/jats:italic>\n            ) model updates, such as news recommenders.\n          <\/jats:p>\n          <jats:p>\n            This article presents\n            <jats:sc>FLeet<\/jats:sc>\n            , the first\n            <jats:italic>Online FL<\/jats:italic>\n            system, acting as a middleware between the Android operating system and the machine learning application.\n            <jats:sc>FLeet<\/jats:sc>\n            combines the privacy of Standard FL with the precision of online learning thanks to two core components: (1)\n            <jats:sc>I-Prof<\/jats:sc>\n            , a new lightweight profiler that predicts and controls the impact of learning tasks on mobile devices, and (2)\n            <jats:sc>AdaSGD<\/jats:sc>\n            , a new adaptive learning algorithm that is resilient to delayed updates.\n          <\/jats:p>\n          <jats:p>\n            Our extensive evaluation shows that Online FL, as implemented by\n            <jats:sc>FLeet<\/jats:sc>\n            , can deliver a 2.3\u00d7 quality boost compared to Standard FL while only consuming 0.036% of the battery per day.\n            <jats:sc>I-Prof<\/jats:sc>\n            can accurately control the impact of learning tasks by improving the prediction accuracy by up to 3.6\u00d7 in terms of computation time, and by up to 19\u00d7 in terms of energy.\n            <jats:sc>AdaSGD<\/jats:sc>\n            outperforms alternative FL approaches by 18.4% in terms of convergence speed on heterogeneous data.\n          <\/jats:p>","DOI":"10.1145\/3527621","type":"journal-article","created":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T15:36:39Z","timestamp":1650641799000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":34,"title":["FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction"],"prefix":"10.1145","volume":"13","author":[{"given":"Georgios","family":"Damaskinos","sequence":"first","affiliation":[{"name":"Facebook, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rachid","family":"Guerraoui","sequence":"additional","affiliation":[{"name":"EPFL, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne-Marie","family":"Kermarrec","sequence":"additional","affiliation":[{"name":"EPFL, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7996-3963","authenticated-orcid":false,"given":"Vlad","family":"Nitu","sequence":"additional","affiliation":[{"name":"INSA Lyon, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rhicheek","family":"Patra","sequence":"additional","affiliation":[{"name":"EPFL, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francois","family":"Taiani","sequence":"additional","affiliation":[{"name":"University of Rennes, Inria, CNRS, IRISA, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,9,23]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"265","volume-title":"Proceedings of OSDI","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, et\u00a0al. 2016. 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