{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:42:31Z","timestamp":1740102151250,"version":"3.37.3"},"reference-count":39,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T00:00:00Z","timestamp":1690761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T00:00:00Z","timestamp":1690761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100011688","name":"ECSEL Joint Under-taking (JU)","doi-asserted-by":"publisher","award":["101007273"],"award-info":[{"award-number":["101007273"]}],"id":[{"id":"10.13039\/501100011688","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,7,31]]},"DOI":"10.1109\/csr57506.2023.10224953","type":"proceedings-article","created":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T17:50:16Z","timestamp":1693245016000},"page":"624-629","source":"Crossref","is-referenced-by-count":0,"title":["Privacy-preserving Federated Learning System for Fatigue Detection"],"prefix":"10.1109","author":[{"given":"Mohammadreza","family":"Mohammadi","sequence":"first","affiliation":[{"name":"RISE Research Institute of Sweden,Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roberto","family":"Allocca","sequence":"additional","affiliation":[{"name":"University of Naples Federico II,Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Eklund","sequence":"additional","affiliation":[{"name":"RISE Research Institute of Sweden,Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rakesh","family":"Shrestha","sequence":"additional","affiliation":[{"name":"RISE Research Institute of Sweden,Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sima","family":"Sinaei","sequence":"additional","affiliation":[{"name":"RISE Research Institute of Sweden,Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"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":"ref35","article-title":"Automatic differentiation in pytorch","author":"paszke","year":"2017","journal-title":"NIPS-W"},{"key":"ref12","article-title":"Federated learning: Strategies for improving communication efficiency","author":"kone?ny","year":"2016","journal-title":"ArXiv Preprint"},{"doi-asserted-by":"publisher","key":"ref34","DOI":"10.3109\/00207459008994241"},{"key":"ref15","article-title":"Security and privacy-enhanced federated learning for anomaly detection in iot infrastructures","author":"cui","year":"2021","journal-title":"IEEE Transactions on Industrial Informatics"},{"year":"2021","journal-title":"HACKEN","article-title":"Advanced drowsiness detection","key":"ref37"},{"doi-asserted-by":"publisher","key":"ref14","DOI":"10.1109\/JSAC.2019.2904348"},{"year":"2022","author":"koukyosyumei","journal-title":"Aijack Security and privacy risk simulator for machine learning","key":"ref36"},{"year":"2022","author":"krishna","journal-title":"Vision transformers and yolov5 based driver drowsiness detection framework","key":"ref31"},{"year":"2022","author":"yao","journal-title":"In-vehicle alertness monitoring for older adults","key":"ref30"},{"doi-asserted-by":"publisher","key":"ref11","DOI":"10.1109\/MCE.2021.3094778"},{"doi-asserted-by":"publisher","key":"ref33","DOI":"10.1109\/LSENS.2021.3070419"},{"doi-asserted-by":"publisher","key":"ref10","DOI":"10.1109\/TII.2021.3073925"},{"doi-asserted-by":"publisher","key":"ref32","DOI":"10.9741\/2766-7227.1021"},{"doi-asserted-by":"publisher","key":"ref2","DOI":"10.1109\/ACCESS.2019.2914373"},{"key":"ref1","first-page":"4245","article-title":"Driver drowsiness detection system and techniques: a review","volume":"5","author":"saini","year":"2014","journal-title":"International Journal of Computer Science and Information Technologies"},{"doi-asserted-by":"publisher","key":"ref17","DOI":"10.1109\/GlobalSIP.2013.6736861"},{"doi-asserted-by":"publisher","key":"ref39","DOI":"10.1145\/2810103.2813677"},{"doi-asserted-by":"publisher","key":"ref16","DOI":"10.1145\/2976749.2978318"},{"key":"ref38","first-page":"1755","article-title":"Dlib-ml: A machine learning toolkit","volume":"10","author":"king","year":"2009","journal-title":"Journal of Machine Learning Research"},{"key":"ref19","article-title":"Generative models for effective ml on private, decentralized datasets","author":"augenstein","year":"2019","journal-title":"ArXiv Preprint"},{"doi-asserted-by":"publisher","key":"ref18","DOI":"10.1109\/ACCESS.2022.3192454"},{"doi-asserted-by":"publisher","key":"ref24","DOI":"10.1109\/EMBC.2012.6347469"},{"doi-asserted-by":"publisher","key":"ref23","DOI":"10.3390\/s131216494"},{"doi-asserted-by":"publisher","key":"ref26","DOI":"10.1109\/CVPRW.2019.00027"},{"doi-asserted-by":"publisher","key":"ref25","DOI":"10.1109\/TITS.2016.2582900"},{"key":"ref20","article-title":"Scalable and differentially private distributed aggregation in the shuffled model","author":"ghazi","year":"2019","journal-title":"ArXiv Preprint"},{"doi-asserted-by":"publisher","key":"ref22","DOI":"10.1109\/TITS.2018.2868499"},{"doi-asserted-by":"publisher","key":"ref21","DOI":"10.1109\/ICDCS.2019.00159"},{"doi-asserted-by":"publisher","key":"ref28","DOI":"10.1109\/IVS.2004.1336359"},{"key":"ref27","article-title":"Driver's fatigue detection based on yawning extraction","author":"noureldin","year":"2014","journal-title":"International Journal of Vehicular Technology"},{"year":"0","journal-title":"UTA Real-Life Drowsiness Dataset","article-title":"UTA-RLDD","key":"ref29"},{"doi-asserted-by":"publisher","key":"ref8","DOI":"10.1109\/MSEC.2020.3039941"},{"doi-asserted-by":"publisher","key":"ref7","DOI":"10.1109\/SP.2019.00065"},{"doi-asserted-by":"publisher","key":"ref9","DOI":"10.1016\/j.ins.2020.12.007"},{"doi-asserted-by":"publisher","key":"ref4","DOI":"10.1016\/j.future.2022.08.009"},{"key":"ref3","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Artificial Intelligence and Statistics"},{"doi-asserted-by":"publisher","key":"ref6","DOI":"10.1109\/CCIS53392.2021.9754649"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.1109\/ITSC48978.2021.9564936"}],"event":{"name":"2023 IEEE International Conference on Cyber Security and Resilience (CSR)","start":{"date-parts":[[2023,7,31]]},"location":"Venice, Italy","end":{"date-parts":[[2023,8,2]]}},"container-title":["2023 IEEE International Conference on Cyber Security and Resilience (CSR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10224900\/10224823\/10224953.pdf?arnumber=10224953","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T17:51:29Z","timestamp":1695664289000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10224953\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,31]]},"references-count":39,"URL":"https:\/\/doi.org\/10.1109\/csr57506.2023.10224953","relation":{},"subject":[],"published":{"date-parts":[[2023,7,31]]}}}