{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T02:24:55Z","timestamp":1771467895396,"version":"3.50.1"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"32","license":[{"start":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T00:00:00Z","timestamp":1723420800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T00:00:00Z","timestamp":1723420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100007801","name":"Fundaci\u00f3n S\u00e9neca","doi-asserted-by":"publisher","award":["21629\/FPI\/21"],"award-info":[{"award-number":["21629\/FPI\/21"]}],"id":[{"id":"10.13039\/100007801","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007801","name":"Fundaci\u00f3n S\u00e9neca","doi-asserted-by":"publisher","award":["21628\/FPI\/21"],"award-info":[{"award-number":["21628\/FPI\/21"]}],"id":[{"id":"10.13039\/100007801","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Swiss Federal Office for Defense Procurement","award":["CYD-C-2020003"],"award-info":[{"award-number":["CYD-C-2020003"]}]},{"name":"University of Z\u00fcrich UZH"},{"DOI":"10.13039\/501100004687","name":"Universidad de Murcia","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004687","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In response to the global safety concern of drowsiness during driving, the European Union enforces that new vehicles must integrate detection systems compliant with the general data protection regulation. To identify drowsiness patterns while preserving drivers\u2019 data privacy, recent literature has combined Federated Learning (FL) with different biosignals, such as facial expressions, heart rate, electroencephalography (EEG), or electrooculography (EOG). However, existing solutions are unsuitable for drowsiness detection where heterogeneous stakeholders want to collaborate at different levels while guaranteeing data privacy. There is a lack of works evaluating the benefits of using Hierarchical FL (HFL) with EEG and EOG biosignals, and comparing HFL over traditional FL and Machine Learning (ML) approaches to detect drowsiness at the wheel while ensuring data confidentiality. Thus, this work proposes a flexible framework for drowsiness identification by using HFL, FL, and ML over EEG and EOG data. To validate the framework, this work defines a scenario of three transportation companies aiming to share data from their drivers without compromising their confidentiality, defining a two-level hierarchical structure. This study presents three incremental Use Cases (UCs) to assess detection performance: UC1) intra-company FL, yielding a 77.3% accuracy while ensuring the privacy of individual drivers\u2019 data; UC2) inter-company FL, achieving 71.7% accuracy for known drivers and 67.1% for new subjects, ensuring data confidentiality between companies but not intra-organization; and UC3) HFL inter-company, which ensured comprehensive data privacy both within and between companies, with an accuracy of 71.9% for training subjects and 65.5% for new subjects.<\/jats:p>","DOI":"10.1007\/s00521-024-10282-3","type":"journal-article","created":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T18:02:37Z","timestamp":1723485757000},"page":"20425-20437","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Privacy-preserving hierarchical federated learning with biosignals to detect drowsiness while driving"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1869-1965","authenticated-orcid":false,"given":"Sergio","family":"L\u00f3pez Bernal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 Manuel","family":"Hidalgo Rogel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enrique Tom\u00e1s","family":"Mart\u00ednez Beltr\u00e1n","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mario","family":"Quiles P\u00e9rez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gregorio","family":"Mart\u00ednez P\u00e9rez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alberto","family":"Huertas Celdr\u00e1n","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,12]]},"reference":[{"key":"10282_CR1","unstructured":"Institute II (2022) Facts + statistics: Drowsy driving. https:\/\/www.iii.org\/fact-statistic\/facts-statistics-drowsy-driving"},{"key":"10282_CR2","unstructured":"Council of the European Union: Regulation (eu) 2019\/2144 of the european parliament and of the council of 27 november 2019 on type-approval requirements for motor vehicles and their trailers, and systems, components and separate technical units intended for such vehicles, as regards their general safety and the protection of vehicle occupants and vulnerable road users. Official Journal L325 L 325, 1\u201340"},{"key":"10282_CR3","unstructured":"Council of the European Union: Regulation (EU) 2016\/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data. Official Journal L325 L 119\/1, 1\u201388"},{"key":"10282_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/s0166-4115(08)62386-9","author":"SG Hart","year":"1988","unstructured":"Hart SG, Staveland LE (1988) Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research. Elsevier. https:\/\/doi.org\/10.1016\/s0166-4115(08)62386-9","journal-title":"Elsevier"},{"key":"10282_CR5","doi-asserted-by":"publisher","unstructured":"Ib\u00e1\u00f1ez V, Silva J, Cauli O (2018) A survey on sleep assessment methods. PeerJ 6. https:\/\/doi.org\/10.7717\/peerj.4849","DOI":"10.7717\/peerj.4849"},{"key":"10282_CR6","doi-asserted-by":"publisher","first-page":"61904","DOI":"10.1109\/ACCESS.2019.2914373","volume":"7","author":"M Ramzan","year":"2019","unstructured":"Ramzan M, Khan HU, Awan SM, Ismail A, Ilyas M, Mahmood A (2019) A survey on state-of-the-art drowsiness detection techniques. IEEE Access 7:61904\u201361919. https:\/\/doi.org\/10.1109\/ACCESS.2019.2914373","journal-title":"IEEE Access"},{"key":"10282_CR7","doi-asserted-by":"crossref","unstructured":"Hidalgo\u00a0Rogel JM, Mart\u00ednez\u00a0Beltr\u00e1n ET, Quiles\u00a0P\u00e9rez M, L\u00f3pez\u00a0Bernal S, Mart\u00ednez\u00a0P\u00e9rez G, Huertas\u00a0Celdr\u00e1n A (2022) Studying Drowsiness Detection Performance while Driving through Scalable Machine Learning Models using Electroencephalography","DOI":"10.2139\/ssrn.3988495"},{"key":"10282_CR8","doi-asserted-by":"publisher","first-page":"108693","DOI":"10.1016\/j.comnet.2021.108693","volume":"204","author":"V Rey","year":"2022","unstructured":"Rey V, S\u00e1nchez S\u00e1nchez PM, Huertas Celdr\u00e1n A, Bovet G (2022) Federated learning for malware detection in IoT devices. Comput Netw 204:108693. https:\/\/doi.org\/10.1016\/j.comnet.2021.108693","journal-title":"Comput Netw"},{"key":"10282_CR9","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez Beltr\u00e1n ET, Quiles P\u00e9rez M, S\u00e1nchez S\u00e1nchez PM, L\u00f3pez Bernal S, Bovet G, Gil P\u00e9rez M, Mart\u00ednez P\u00e9rez G, Huertas Celdr\u00e1n A (2023) Decentralized Federated Learning: Fundamentals, State of the Art. Trends, and Challenges, Frameworks","DOI":"10.1016\/j.eswa.2023.122861"},{"key":"10282_CR10","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.future.2022.08.009","volume":"138","author":"C Zhao","year":"2023","unstructured":"Zhao C, Gao Z, Wang Q, Xiao K, Mo Z, Deen MJ (2023) Fedsup: a communication-efficient federated learning fatigue driving behaviors supervision approach. Futur Gener Comput Syst 138:52\u201360. https:\/\/doi.org\/10.1016\/j.future.2022.08.009","journal-title":"Futur Gener Comput Syst"},{"key":"10282_CR11","unstructured":"Hidalgo Rogel JM, L\u00f3pez Bernal S, Mart\u00ednez Beltr\u00e1n ET, Quiles P\u00e9rez M, Mart\u00ednez P\u00e9rez G, Huertas Celdr\u00e1n A (2023) CyberDataLab\/drowsiness-hfl. https:\/\/github.com\/CyberDataLab\/drowsiness-hfl"},{"issue":"2","key":"10282_CR12","doi-asserted-by":"publisher","first-page":"026017","DOI":"10.1088\/1741-2552\/aa5a98","volume":"14","author":"W-L Zheng","year":"2017","unstructured":"Zheng W-L, Lu B-L (2017) A multimodal approach to estimating vigilance using EEG and forehead EOG. J Neural Eng 14(2):026017. https:\/\/doi.org\/10.1088\/1741-2552\/aa5a98","journal-title":"J Neural Eng"},{"key":"10282_CR13","doi-asserted-by":"publisher","unstructured":"Liu L, Zhang J, Song SH, Letaief KB (2020) Client-edge-cloud hierarchical federated learning. In: ICC 2020 - 2020 IEEE International Conference on Communications (ICC), pp. 1\u20136. https:\/\/doi.org\/10.1109\/ICC40277.2020.9148862","DOI":"10.1109\/ICC40277.2020.9148862"},{"key":"10282_CR14","doi-asserted-by":"publisher","unstructured":"Wainakh A, Guinea AS, Grube T, M\u00fchlh\u00e4user M (2020) Enhancing privacy via hierarchical federated learning. In: 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS &PW), pp. 344\u2013347. https:\/\/doi.org\/10.1109\/EuroSPW51379.2020.00053","DOI":"10.1109\/EuroSPW51379.2020.00053"},{"key":"10282_CR15","doi-asserted-by":"publisher","first-page":"110763","DOI":"10.1016\/j.knosys.2023.110763","volume":"276","author":"Y Li","year":"2023","unstructured":"Li Y, Wang X, Sun R, Xie X, Ying S, Ren S (2023) Trustiness-based hierarchical decentralized federated learning. Knowl-Based Syst 276:110763. https:\/\/doi.org\/10.1016\/j.knosys.2023.110763","journal-title":"Knowl-Based Syst"},{"key":"10282_CR16","doi-asserted-by":"publisher","unstructured":"Gao H, Liu Y, Sisbot EA, Farid YZ, Oguchi K, Han Z (2023) Hierarchical federated learning with mean field game device selection for connected vehicle applications. In: 2023 IEEE Intelligent Vehicles Symposium (IV), pp. 1\u20136. https:\/\/doi.org\/10.1109\/IV55152.2023.10186687","DOI":"10.1109\/IV55152.2023.10186687"},{"issue":"5","key":"10282_CR17","doi-asserted-by":"publisher","first-page":"5600","DOI":"10.1109\/TITS.2023.3243003","volume":"24","author":"H Zhou","year":"2023","unstructured":"Zhou H, Zheng Y, Huang H, Shu J, Jia X (2023) Toward robust hierarchical federated learning in internet of vehicles. IEEE Trans Intell Transp Syst 24(5):5600\u20135614. https:\/\/doi.org\/10.1109\/TITS.2023.3243003","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"10282_CR18","doi-asserted-by":"publisher","unstructured":"Zafar A, Prehofer C, Cheng C-H (2021) Federated learning for driver status monitoring. In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), pp. 1463\u20131469. https:\/\/doi.org\/10.1109\/ITSC48978.2021.9564936","DOI":"10.1109\/ITSC48978.2021.9564936"},{"key":"10282_CR19","doi-asserted-by":"publisher","first-page":"80565","DOI":"10.1109\/ACCESS.2022.3192454","volume":"10","author":"L Zhang","year":"2022","unstructured":"Zhang L, Saito H, Yang L, Wu J (2022) Privacy-preserving federated transfer learning for driver drowsiness detection. IEEE Access 10:80565\u201380574. https:\/\/doi.org\/10.1109\/ACCESS.2022.3192454","journal-title":"IEEE Access"},{"key":"10282_CR20","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2023.3273727","author":"TA Khoa","year":"2023","unstructured":"Khoa TA, Trac ND, Tinh VP, Nam NH, Dang DNM, Son HH, Lam PD (2023) Safety is our friend: a federated learning framework toward driver\u2019s state and behavior detection. IEEE Trans Comput Soc Syst. https:\/\/doi.org\/10.1109\/TCSS.2023.3273727","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"10282_CR21","doi-asserted-by":"publisher","first-page":"105881","DOI":"10.1016\/j.engappai.2023.105881","volume":"120","author":"R Chhabra","year":"2023","unstructured":"Chhabra R, Singh S, Khullar V (2023) Privacy enabled driver behavior analysis in heterogeneous iov using federated learning. Eng Appl Artif Intell 120:105881. https:\/\/doi.org\/10.1016\/j.engappai.2023.105881","journal-title":"Eng Appl Artif Intell"},{"key":"10282_CR22","unstructured":"Compumedics: Compumedics Neuroscan (2023). https:\/\/compumedicsneuroscan.com\/"},{"key":"10282_CR23","unstructured":"MNE: Open-source Python package for exploring, visualizing, and analyzing human neurophysiological data: MEG, EEG, sEEG, ECoG, NIRS, and more (2023). https:\/\/mne.tools\/stable\/index.html"},{"key":"10282_CR24","unstructured":"NeuroKit2: The python toolbox for neurophysiological signal processing (2023). https:\/\/pypi.org\/project\/neurokit2\/"},{"issue":"12","key":"10282_CR25","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1049\/iet-ipr.2018.5245","volume":"12","author":"WH Gu","year":"2018","unstructured":"Gu WH, Zhu Y, Chen XD, He LF, Zheng BB (2018) Hierarchical CNN-based real-time fatigue detection system by visual-based technologies using MSP model. IET Image Proc 12(12):2319\u20132329. https:\/\/doi.org\/10.1049\/iet-ipr.2018.5245","journal-title":"IET Image Proc"},{"key":"10282_CR26","doi-asserted-by":"publisher","first-page":"113204","DOI":"10.1016\/j.eswa.2020.113204","volume":"147","author":"F Zhou","year":"2020","unstructured":"Zhou F, Alsaid A, Blommer M, Curry R, Swaminathan R, Kochhar D, Talamonti W, Tijerina L, Lei B (2020) Driver fatigue transition prediction in highly automated driving using physiological features. Expert Syst Appl 147:113204. https:\/\/doi.org\/10.1016\/j.eswa.2020.113204","journal-title":"Expert Syst Appl"},{"key":"10282_CR27","unstructured":"Beutel DJ, Topal T, Mathur A, Qiu X, Fernandez-Marques J, Gao Y, Sani L, Li KH, Parcollet T, Porto Buarque\u00a0de Gusm\u00e3o P, D.\u00a0Lane N (2022) Flower: a friendly federated learning research framework"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10282-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10282-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10282-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,28]],"date-time":"2024-09-28T08:09:39Z","timestamp":1727510979000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10282-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,12]]},"references-count":27,"journal-issue":{"issue":"32","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["10282"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10282-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,12]]},"assertion":[{"value":"7 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}