{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T09:44:27Z","timestamp":1730281467383,"version":"3.28.0"},"reference-count":16,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T00:00:00Z","timestamp":1687737600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T00:00:00Z","timestamp":1687737600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,26]]},"DOI":"10.1109\/med59994.2023.10185810","type":"proceedings-article","created":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T17:21:50Z","timestamp":1690305710000},"page":"593-598","source":"Crossref","is-referenced-by-count":1,"title":["FedAcc and FedAccSize: Aggregation Methods for Federated Learning Applications"],"prefix":"10.1109","author":[{"given":"Iuliana","family":"Bejenar","sequence":"first","affiliation":[{"name":"“Gheorghe Asachi” Technical University of Iasi,Department of Automatic Control and Applied Informatics,Iasi,Romania"}]},{"given":"Lavinia","family":"Ferariu","sequence":"additional","affiliation":[{"name":"“Gheorghe Asachi” Technical University of Iasi,Department of Automatic Control and Applied Informatics,Iasi,Romania"}]},{"given":"Carlos","family":"Pascal","sequence":"additional","affiliation":[{"name":"“Gheorghe Asachi” Technical University of Iasi,Department of Automatic Control and Applied Informatics,Iasi,Romania"}]},{"given":"Constantin F.","family":"Caruntu","sequence":"additional","affiliation":[{"name":"“Gheorghe Asachi” Technical University of Iasi,Department of Automatic Control and Applied Informatics,Iasi,Romania"}]}],"member":"263","reference":[{"key":"ref13","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"li","year":"2020","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.07.098"},{"key":"ref15","first-page":"2938","article-title":"How to backdoor federated learning","author":"bagdasaryan","year":"2020","journal-title":"International Conference on Artificial Intelligence and Statistics"},{"key":"ref14","article-title":"Federated learning with matched averaging","author":"wang","year":"2020","journal-title":"arXiv preprint arXiv 2002 06755"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2023.01.019"},{"key":"ref10","article-title":"Recent advances on federated learning: A systematic survey","author":"liu","year":"2023","journal-title":"arXiv preprint arXiv 2301 07898"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"11191","DOI":"10.3390\/app112311191","article-title":"A systematic review of federated learning in the healthcare area: From the perspective of data properties and applications","volume":"11","author":"shyu","year":"2021","journal-title":"Applied Sciences"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00323-1"},{"key":"ref16","article-title":"Federated learning of deep networks using model averaging","volume":"2","author":"mcmahan","year":"2016","journal-title":"arXiv preprint arXiv 1602 05629"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref7","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref9","article-title":"Learning differentially private recurrent language models","author":"mcmahan","year":"2017","journal-title":"arXiv preprint arXiv 1710 06963"},{"key":"ref4","article-title":"Applied federated learning: Improving google keyboard query suggestions","author":"yang","year":"2018","journal-title":"arXiv preprint arXiv 1812 02588"},{"key":"ref3","article-title":"Federated learning for mobile keyboard prediction","author":"hard","year":"2018","journal-title":"arXiv preprint arXiv 1811 03604"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.3390\/s20216230"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM38437.2019.9013587"}],"event":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","start":{"date-parts":[[2023,6,26]]},"location":"Limassol, Cyprus","end":{"date-parts":[[2023,6,29]]}},"container-title":["2023 31st Mediterranean Conference on Control and Automation (MED)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10185661\/10185641\/10185810.pdf?arnumber=10185810","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T17:35:50Z","timestamp":1692034550000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10185810\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,26]]},"references-count":16,"URL":"https:\/\/doi.org\/10.1109\/med59994.2023.10185810","relation":{},"subject":[],"published":{"date-parts":[[2023,6,26]]}}}