{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T07:24:13Z","timestamp":1777361053515,"version":"3.51.4"},"reference-count":72,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T00:00:00Z","timestamp":1703116800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T00:00:00Z","timestamp":1703116800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s11227-023-05847-3","type":"journal-article","created":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T11:02:15Z","timestamp":1703156535000},"page":"10255-10274","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A privacy-preserved IoMT-based mental stress detection framework with federated learning"],"prefix":"10.1007","volume":"80","author":[{"given":"Abdulrahman","family":"Alahmadi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haroon Ahmed","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ghufran","family":"Shafiq","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junaid","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bakhtiar","family":"Ali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad Awais","family":"Javed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Zubair","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rayan Hamza","family":"Alsisi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed H.","family":"Alahmadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,21]]},"reference":[{"key":"5847_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TITS.2021.3110942","volume":"5","author":"WU Khan","year":"2021","unstructured":"Khan WU, Javed MA, Nguyen TN, Khan S, Elhalawany BM (2021) Energy-efficient resource allocation for 6g backscatter-enabled noma iov networks. IEEE Trans Intell Transp Syst 5:1\u201311. https:\/\/doi.org\/10.1109\/TITS.2021.3110942","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"5847_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TII.2022.3151773","volume":"1","author":"MA Javed","year":"2022","unstructured":"Javed MA, Nguyen TN, Mirza J, Ahmed J, Ali B (2022) Reliable communications for cybertwin driven 6g iovs using intelligent reflecting surfaces. IEEE Trans Indust Inform 1:1\u20131. https:\/\/doi.org\/10.1109\/TII.2022.3151773","journal-title":"IEEE Trans Indust Inform"},{"key":"5847_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JBHI.2022.3197910","volume":"25","author":"TK Dash","year":"2022","unstructured":"Dash TK, Chakraborty C, Mahapatra S, Panda G (2022) Gradient boosting machine and efficient combination of features for speech-based detection of covid-19. IEEE J Biomed Health Inform 25:1\u20138. https:\/\/doi.org\/10.1109\/JBHI.2022.3197910","journal-title":"IEEE J Biomed Health Inform"},{"key":"5847_CR4","doi-asserted-by":"publisher","first-page":"1156","DOI":"10.3390\/electronics11111705","volume":"11","author":"G Rathee","year":"2022","unstructured":"Rathee G, Saini H, Kerrache CA, Herrera-Tapia J (2022) A computational framework for cyber threats in medical iot systems. Electronics 11:1156","journal-title":"Electronics"},{"issue":"22","key":"5847_CR5","doi-asserted-by":"publisher","first-page":"25561","DOI":"10.1109\/JSEN.2021.3117399","volume":"21","author":"D Chen","year":"2021","unstructured":"Chen D, Zhuang Y, Huai J, Sun X, Yang X, Awais Javed M, Brown J, Sheng Z, Thompson J (2021) Coexistence and interference mitigation for wpans and wlans from traditional approaches to deep learning: A review. IEEE Sens J 21(22):25561\u201325589. https:\/\/doi.org\/10.1109\/JSEN.2021.3117399","journal-title":"IEEE Sens J"},{"key":"5847_CR6","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.comnet.2018.07.017","volume":"144","author":"A \u010colakovi\u0107","year":"2018","unstructured":"\u010colakovi\u0107 A, Had\u017eiali\u0107 M (2018) Internet of things (iot): A review of enabling technologies, challenges, and open research issues. Comput Netw 144:17\u201339","journal-title":"Comput Netw"},{"key":"5847_CR7","doi-asserted-by":"publisher","first-page":"23022","DOI":"10.1109\/ACCESS.2020.2970118","volume":"8","author":"K Shafique","year":"2020","unstructured":"Shafique K, Khawaja BA, Sabir F, Qazi S, Mustaqim M (2020) Internet of things (iot) for next-generation smart systems: A review of current challenges, future trends and prospects for emerging 5g-iot scenarios. IEEE Access 8:23022\u201323040","journal-title":"IEEE Access"},{"issue":"3","key":"5847_CR8","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1109\/MITP.2020.3031358","volume":"23","author":"T Saba","year":"2021","unstructured":"Saba T, Haseeb K, Shah AA, Rehman A, Tariq U, Mehmood Z (2021) A machine-learning-based approach for autonomous iot security. IT Professional 23(3):69\u201375","journal-title":"IT Professional"},{"issue":"6","key":"5847_CR9","doi-asserted-by":"publisher","first-page":"2115","DOI":"10.3390\/s22062115","volume":"22","author":"K Haseeb","year":"2022","unstructured":"Haseeb K, Rehman A, Saba T, Bahaj SA, Lloret J (2022) Device-to-device (d2d) multi-criteria learning algorithm using secured sensors. Sensors 22(6):2115","journal-title":"Sensors"},{"issue":"7","key":"5847_CR10","doi-asserted-by":"publisher","first-page":"2615","DOI":"10.1109\/JBHI.2020.3040015","volume":"25","author":"Z Yan","year":"2021","unstructured":"Yan Z, Wicaksana J, Wang Z, Yang X, Cheng K-T (2021) Variation-aware federated learning with multi-source decentralized medical image data. IEEE J Biomed Health Inf 25(7):2615\u20132628. https:\/\/doi.org\/10.1109\/JBHI.2020.3040015","journal-title":"IEEE J Biomed Health Inf"},{"issue":"21","key":"5847_CR11","doi-asserted-by":"publisher","first-page":"15884","DOI":"10.1109\/JIOT.2021.3056185","volume":"8","author":"W Zhang","year":"2021","unstructured":"Zhang W, Zhou T, Lu Q, Wang X, Zhu C, Sun H, Wang Z, Lo SK, Wang F-Y (2021) Dynamic-fusion-based federated learning for covid-19 detection. IEEE Internet Things J 8(21):15884\u201315891. https:\/\/doi.org\/10.1109\/JIOT.2021.3056185","journal-title":"IEEE Internet Things J"},{"key":"5847_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JBHI.2022.3187471","volume":"5","author":"Z Xu","year":"2022","unstructured":"Xu Z, Guo Y, Chakraborty C, Hua Q, Chen S, Yu K (2022) A simple federated learning-based scheme for security enhancement over internet of medical things. IEEE J Biomed Health Inform 5:1\u201313. https:\/\/doi.org\/10.1109\/JBHI.2022.3187471","journal-title":"IEEE J Biomed Health Inform"},{"key":"5847_CR13","doi-asserted-by":"crossref","unstructured":"Barka E, Dahmane S, Kerrache CA, Khayat M, Sallabi F (2021) Sthm: A secured and trusted healthcare monitoring architecture using sdn and blockchain. Electronics 10, (15)","DOI":"10.3390\/electronics10151787"},{"key":"5847_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JBHI.2022.3149288","volume":"2","author":"MN Hossen","year":"2022","unstructured":"Hossen MN, Panneerselvam V, Koundal D, Ahmed K, Bui FM, Ibrahim SM (2022) Federated machine learning for detection of skin diseases and enhancement of internet of medical things (iomt) security. IEEE J Biomed Health Inform 2:1\u20131. https:\/\/doi.org\/10.1109\/JBHI.2022.3149288","journal-title":"IEEE J Biomed Health Inform"},{"issue":"3","key":"5847_CR15","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/IOTM.0100.2000154","volume":"4","author":"Y Yang","year":"2021","unstructured":"Yang Y, Wang X, Ning Z, Rodrigues JJPC, Jiang X, Guo Y (2021) Edge learning for internet of medical things and its covid-19 applications: A distributed 3c framework. IEEE Internet Things Mag 4(3):18\u201323. https:\/\/doi.org\/10.1109\/IOTM.0100.2000154","journal-title":"IEEE Internet Things Mag"},{"key":"5847_CR16","doi-asserted-by":"publisher","first-page":"13129","DOI":"10.1109\/ACCESS.2017.2789329","volume":"6","author":"JJ Rodrigues","year":"2018","unstructured":"Rodrigues JJ, Segundo DBDR, Junqueira HA, Sabino MH, Prince RM, Al-Muhtadi J, De Albuquerque VHC (2018) Enabling technologies for the internet of health things. IEEE Access 6:13129\u201313141","journal-title":"IEEE Access"},{"issue":"4","key":"5847_CR17","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1109\/MCOMSTD.2018.1800040","volume":"2","author":"MA Javed","year":"2018","unstructured":"Javed MA, Zeadally S (2018) Repguide: Reputation-based route guidance using internet of vehicles. IEEE Commun Stand Mag 2(4):81\u201387. https:\/\/doi.org\/10.1109\/MCOMSTD.2018.1800040","journal-title":"IEEE Commun Stand Mag"},{"key":"5847_CR18","first-page":"1","volume":"2","author":"S Razdan","year":"2021","unstructured":"Razdan S, Sharma S (2021) Internet of medical things (iomt): Overview, emerging technologies, and case studies. IETE Tech Rev 2:1\u201314","journal-title":"IETE Tech Rev"},{"key":"5847_CR19","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.eswa.2019.04.057","volume":"137","author":"Y Wang","year":"2019","unstructured":"Wang Y, Cang S, Yu H (2019) A survey on wearable sensor modality centred human activity recognition in health care. Expert Syst Appl 137:167\u2013190","journal-title":"Expert Syst Appl"},{"key":"5847_CR20","doi-asserted-by":"crossref","unstructured":"Hernandez L, Cao H, Wachowicz M (2017) Implementing an edge-fog-cloud architecture for stream data management. In: 2017 IEEE Fog World Congress (FWC), pp. 1\u20136. IEEE","DOI":"10.1109\/FWC.2017.8368538"},{"key":"5847_CR21","doi-asserted-by":"crossref","unstructured":"Motti VG (2020) Introduction to wearable computers, 1\u201339","DOI":"10.1007\/978-3-030-27111-4_1"},{"key":"5847_CR22","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.future.2018.07.049","volume":"90","author":"AA Mutlag","year":"2019","unstructured":"Mutlag AA, Abd Ghani MK, Na Arunkumar, Mohammed MA, Mohd O (2019) Enabling technologies for fog computing in healthcare iot systems. Future Gen Comput Syst 90:62\u201378","journal-title":"Future Gen Comput Syst"},{"key":"5847_CR23","doi-asserted-by":"publisher","first-page":"5337733","DOI":"10.1155\/2022\/5337733","volume":"2022","author":"A Elhadad","year":"2022","unstructured":"Elhadad A, Alanazi F, Taloba AI, Abozeid A (2022) Fog computing service in the healthcare monitoring system for managing the real-time notification. J Healthc Eng 2022:5337733","journal-title":"J Healthc Eng"},{"key":"5847_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2019.102938","volume":"72","author":"B Farahani","year":"2020","unstructured":"Farahani B, Barzegari M, Aliee FS, Shaik KA (2020) Towards collaborative intelligent iot ehealth: From device to fog, and cloud. Microprocess Microsyst 72:102938","journal-title":"Microprocess Microsyst"},{"key":"5847_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2020.100251","volume":"11","author":"E Moghadas","year":"2020","unstructured":"Moghadas E, Rezazadeh J, Farahbakhsh R (2020) An iot patient monitoring based on fog computing and data mining: Cardiac arrhythmia usecase. Internet Things 11:100251","journal-title":"Internet Things"},{"key":"5847_CR26","doi-asserted-by":"crossref","unstructured":"Karthick G, Pankajavalli P (2020) Architecting iot based healthcare systems using machine learning algorithms: cloud-oriented healthcare model, streaming data analytics architecture, and case study, 40\u201366 (2020)","DOI":"10.4018\/978-1-7998-1090-2.ch003"},{"issue":"1","key":"5847_CR27","first-page":"414","volume":"20","author":"O AlShorman","year":"2020","unstructured":"AlShorman O, AlShorman B, Alkhassaweneh M, Alkahtani F (2020) A review of internet of medical things (iomt)-based remote health monitoring through wearable sensors: A case study for diabetic patients. Indones J Electr Eng Comput Sci 20(1):414\u2013422","journal-title":"Indones J Electr Eng Comput Sci"},{"key":"5847_CR28","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.future.2019.06.004","volume":"101","author":"L Syed","year":"2019","unstructured":"Syed L, Jabeen S, Manimala S, Alsaeedi A (2019) Smart healthcare framework for ambient assisted living using iomt and big data analytics techniques. Futur Gen Comput Syst 101:136\u2013151","journal-title":"Futur Gen Comput Syst"},{"key":"5847_CR29","doi-asserted-by":"crossref","unstructured":"Awotunde JB, Ogundokun RO, Misra S (2021) Cloud and iomt-based big data analytics system during covid-19 pandemic, 181\u2013201","DOI":"10.1007\/978-3-030-66633-0_8"},{"key":"5847_CR30","doi-asserted-by":"crossref","unstructured":"Bharathi M, Amsaveni A (2021) Machine learning with iomt: Opportunities and research challenges, 235\u2013252","DOI":"10.1007\/978-3-030-63937-2_13"},{"key":"5847_CR31","doi-asserted-by":"publisher","first-page":"39043","DOI":"10.1109\/ACCESS.2021.3062687","volume":"9","author":"Z Yu","year":"2021","unstructured":"Yu Z, Amin SU, Alhussein M, Lv Z (2021) Research on disease prediction based on improved deepfm and iomt. IEEE Access 9:39043\u201339054","journal-title":"IEEE Access"},{"key":"5847_CR32","doi-asserted-by":"crossref","unstructured":"Awotunde JB, Ajagbe SA, Idowu IR, Ndunagu JN (2021) An enhanced cloud-iomt-based and machine learning for effective covid-19 diagnosis system, 55\u201376","DOI":"10.1007\/978-3-030-82800-4_3"},{"key":"5847_CR33","doi-asserted-by":"crossref","unstructured":"Ghantasala GP, Kumari NV, Patan R (2021) Cancer prediction and diagnosis hinged on hcml in iomt environment, 179\u2013207","DOI":"10.1016\/B978-0-12-821229-5.00004-5"},{"issue":"4","key":"5847_CR34","doi-asserted-by":"publisher","first-page":"40","DOI":"10.3390\/bios10040040","volume":"10","author":"A Zamkah","year":"2020","unstructured":"Zamkah A, Hui T, Andrews S, Dey N, Shi F, Sherratt RS (2020) Identification of suitable biomarkers for stress and emotion detection for future personal affective wearable sensors. Biosensors 10(4):40","journal-title":"Biosensors"},{"key":"5847_CR35","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1016\/j.future.2018.03.038","volume":"92","author":"A Costa","year":"2019","unstructured":"Costa A, Rincon JA, Carrascosa C, Julian V, Novais P (2019) Emotions detection on an ambient intelligent system using wearable devices. Futur Gener Comput Syst 92:479\u2013489","journal-title":"Futur Gener Comput Syst"},{"issue":"22","key":"5847_CR36","doi-asserted-by":"publisher","first-page":"25421","DOI":"10.1109\/JSEN.2021.3095853","volume":"21","author":"D Gupta","year":"2021","unstructured":"Gupta D, Bhatia M, Kumar A (2021) Resolving data overload and latency issues in multivariate time-series iomt data for mental health monitoring. IEEE Sens J 21(22):25421\u201325428","journal-title":"IEEE Sens J"},{"key":"5847_CR37","doi-asserted-by":"crossref","unstructured":"Sundaravadivel P, Salvatore P, Indic P (2020) M-sid: an iot-based edge-intelligent framework for suicidal ideation detection. In: 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), pp. 1\u20136. IEEE","DOI":"10.1109\/WF-IoT48130.2020.9221279"},{"issue":"3","key":"5847_CR38","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1080\/24725579.2019.1583703","volume":"9","author":"AD McDonald","year":"2019","unstructured":"McDonald AD, Sasangohar F, Jatav A, Rao AH (2019) Continuous monitoring and detection of post-traumatic stress disorder (ptsd) triggers among veterans: a supervised machine learning approach. IISE Trans Healthc Syst Eng 9(3):201\u2013211","journal-title":"IISE Trans Healthc Syst Eng"},{"key":"5847_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.drugalcdep.2020.107929","volume":"209","author":"S Carreiro","year":"2020","unstructured":"Carreiro S, Chintha KK, Shrestha S, Chapman B, Smelson D, Indic P (2020) Wearable sensor-based detection of stress and craving in patients during treatment for substance use disorder: A mixed methods pilot study. Drug Alcohol Depend 209:107929","journal-title":"Drug Alcohol Depend"},{"key":"5847_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103139","volume":"92","author":"YS Can","year":"2019","unstructured":"Can YS, Arnrich B, Ersoy C (2019) Stress detection in daily life scenarios using smart phones and wearable sensors: A survey. J Biomed Inform 92:103139","journal-title":"J Biomed Inform"},{"key":"5847_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TITS.2022.3165791","volume":"4","author":"F Jameel","year":"2022","unstructured":"Jameel F, Javed MA, Zeadally S, J\u00e4sntti R (2022) Secure transmission in cellular v2x communications using deep q-learning. IEEE Trans Intell Transp Syst 4:1\u201310. https:\/\/doi.org\/10.1109\/TITS.2022.3165791","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"5847_CR42","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.comnet.2018.03.010","volume":"137","author":"MA Javed","year":"2018","unstructured":"Javed MA, Zeadally S, Hamid Z (2018) Trust-based security adaptation mechanism for vehicular sensor networks. Comput Netw 137:27\u201336","journal-title":"Comput Netw"},{"key":"5847_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106775","volume":"216","author":"C Zhang","year":"2021","unstructured":"Zhang C, Xie Y, Bai H, Yu B, Li W, Gao Y (2021) A survey on federated learning. Knowl Based Syst 216:106775","journal-title":"Knowl Based Syst"},{"issue":"2","key":"5847_CR44","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1109\/MITS.2018.2806636","volume":"10","author":"MA Javed","year":"2018","unstructured":"Javed MA, Hamida EB, Al-Fuqaha A, Bhargava B (2018) Adaptive security for intelligent transport system applications. IEEE Intell Transp Syst Mag 10(2):110\u2013120. https:\/\/doi.org\/10.1109\/MITS.2018.2806636","journal-title":"IEEE Intell Transp Syst Mag"},{"issue":"6","key":"5847_CR45","doi-asserted-by":"publisher","first-page":"879","DOI":"10.3390\/s16060879","volume":"16","author":"MA Javed","year":"2016","unstructured":"Javed MA, Hamida EB, Znaidi W (2016) Security in intelligent transport systems for smart cities: From theory to practice. Sensors 16(6):879","journal-title":"Sensors"},{"issue":"6","key":"5847_CR46","doi-asserted-by":"publisher","first-page":"4049","DOI":"10.1002\/ett.4049","volume":"33","author":"M Papaioannou","year":"2022","unstructured":"Papaioannou M, Karageorgou M, Mantas G, Sucasas V, Essop I, Rodriguez J, Lymberopoulos D (2022) A survey on security threats and countermeasures in internet of medical things (iomt). Trans Emerg Telecommun Technol 33(6):4049","journal-title":"Trans Emerg Telecommun Technol"},{"issue":"2","key":"5847_CR47","doi-asserted-by":"publisher","first-page":"39","DOI":"10.3390\/a10020039","volume":"10","author":"S Anwar","year":"2017","unstructured":"Anwar S, Mohamad Zain J, Zolkipli MF, Inayat Z, Khan S, Anthony B, Chang V (2017) From intrusion detection to an intrusion response system: fundamentals, requirements, and future directions. Algorithms 10(2):39","journal-title":"Algorithms"},{"issue":"17","key":"5847_CR48","doi-asserted-by":"publisher","first-page":"4828","DOI":"10.3390\/s20174828","volume":"20","author":"D Koutras","year":"2020","unstructured":"Koutras D, Stergiopoulos G, Dasaklis T, Kotzanikolaou P, Glynos D, Douligeris C (2020) Security in iomt communications: A survey. Sensors 20(17):4828","journal-title":"Sensors"},{"key":"5847_CR49","doi-asserted-by":"publisher","first-page":"44219","DOI":"10.1109\/ACCESS.2020.2977423","volume":"8","author":"L Cao","year":"2020","unstructured":"Cao L, Jiang X, Zhao Y, Wang S, You D, Xu X (2020) A survey of network attacks on cyber-physical systems. IEEE Access 8:44219\u201344227","journal-title":"IEEE Access"},{"issue":"2","key":"5847_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3298981","volume":"10","author":"Q Yang","year":"2019","unstructured":"Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: Concept and applications. ACM Trans Intell Syst Technol TIST 10(2):1\u201319","journal-title":"ACM Trans Intell Syst Technol TIST"},{"key":"5847_CR51","doi-asserted-by":"publisher","unstructured":"Alazab M, R\u00a0M, SP, M P, Reddy P, Gadekallu TR, Pham Q-V (2021) Federated learning for cybersecurity: Concepts, challenges and future directions. IEEE Trans Indust Inform 1\u20131 https:\/\/doi.org\/10.1109\/TII.2021.3119038","DOI":"10.1109\/TII.2021.3119038"},{"issue":"3","key":"5847_CR52","doi-asserted-by":"publisher","first-page":"1622","DOI":"10.1109\/COMST.2021.3075439","volume":"23","author":"DC Nguyen","year":"2021","unstructured":"Nguyen DC, Ding M, Pathirana PN, Seneviratne A, Li J, Poor HV (2021) Federated learning for internet of things: A comprehensive survey. IEEE Commun Surveys Tutor 23(3):1622\u20131658","journal-title":"IEEE Commun Surveys Tutor"},{"key":"5847_CR53","unstructured":"Stress a major health problem in the U.S., warns APA. American Psychological Association. https:\/\/www.apa.org\/news\/press\/releases\/2007\/10\/stress"},{"key":"5847_CR54","unstructured":"Work-related ill health and occupational disease in Great Britain. https:\/\/www.hse.gov.uk\/statistics\/causdis\/"},{"key":"5847_CR55","unstructured":"Alexander R. Illnesses caused by stress. https:\/\/www.everydayhealth.com\/emotional-health\/stress\/illnesses-caused-stress\/"},{"key":"5847_CR56","unstructured":"Stress: Signs, symptoms, management & prevention. https:\/\/my.clevelandclinic.org\/health\/articles\/11874-stress"},{"key":"5847_CR57","unstructured":"Link, R.: The signs and symptoms of too much stress. Healthline Media (2021). https:\/\/www.healthline.com\/nutrition\/symptoms-of-stress"},{"issue":"8","key":"5847_CR58","doi-asserted-by":"publisher","first-page":"59565","DOI":"10.3390\/s19081849","volume":"19","author":"YS Can","year":"2019","unstructured":"Can YS, Chalabianloo N, Ekiz D, Ersoy C (2019) Continuous stress detection using wearable sensors in real life: Algorithmic programming contest case study. Sensors 19(8):59565","journal-title":"Sensors"},{"key":"5847_CR59","doi-asserted-by":"crossref","unstructured":"Schmidt P, Reiss A, Duerichen R, Marberger C, Van\u00a0Laerhoven K (2018) Introducing wesad, a multimodal dataset for wearable stress and affect detection. In: Proceedings of the 20th ACM International Conference on Multimodal Interaction, pp. 400\u2013408","DOI":"10.1145\/3242969.3242985"},{"issue":"1","key":"5847_CR60","first-page":"92","volume":"24","author":"A Anusha","year":"2019","unstructured":"Anusha A, Sukumaran P, Sarveswaran V, Shyam A, Akl TJ, Preejith S, Sivaprakasam M et al (2019) Electrodermal activity based pre-surgery stress detection using a wrist wearable. IEEE J Biomed Health Inform 24(1):92\u2013100","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"5847_CR61","doi-asserted-by":"publisher","first-page":"37524","DOI":"10.1038\/srep37524","volume":"6","author":"G Shafiq","year":"2016","unstructured":"Shafiq G, Tatinati S, Ang WT, Veluvolu KC (2016) Automatic identification of systolic time intervals in seismocardiogram. Sci Rep 6(1):37524","journal-title":"Sci Rep"},{"issue":"1","key":"5847_CR62","first-page":"1","volume":"4","author":"G Shafiq","year":"2017","unstructured":"Shafiq G, Veluvolu KC (2017) Multimodal chest surface motion data for respiratory and cardiovascular monitoring applications. Sci Data 4(1):1\u201312","journal-title":"Sci Data"},{"key":"5847_CR63","doi-asserted-by":"crossref","unstructured":"Farhad A, Woolley S, Andras P (2021) Federated learning for ai to improve patient care using wearable and iomt sensors. In: 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), pp. 434\u2013434. IEEE","DOI":"10.1109\/ICHI52183.2021.00071"},{"key":"5847_CR64","doi-asserted-by":"crossref","unstructured":"Samuel O, Omojo A, Onuja A, Sunday Y, Tiwari P, Gupta D, Hafeez G, Yahaya A, Fatoba O, Shamshirband S (2022) Iomt: A covid-19 healthcare system driven by federated learning and blockchain. IEEE J Biomed Health Inform","DOI":"10.1109\/JBHI.2022.3143576"},{"key":"5847_CR65","doi-asserted-by":"crossref","unstructured":"Hossen MN, Panneerselvam V, Koundal D, Ahmed K, Bui FM, Ibrahim SM (2022) Federated machine learning for detection of skin diseases and enhancement of internet of medical things (iomt) security. IEEE J Biomed Health Inform","DOI":"10.1109\/JBHI.2022.3149288"},{"key":"5847_CR66","doi-asserted-by":"crossref","unstructured":"Ali M, Naeem F, Tariq M, Kaddoum G (2022) Federated learning for privacy preservation in smart healthcare systems: A comprehensive survey. arXiv preprint arXiv:2203.09702","DOI":"10.1109\/JBHI.2022.3181823"},{"key":"5847_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105482","volume":"193","author":"S Pourmohammadi","year":"2020","unstructured":"Pourmohammadi S, Maleki A (2020) Stress detection using ecg and emg signals: A comprehensive study. Computer Methods Programs Biomed 193:105482","journal-title":"Computer Methods Programs Biomed"},{"issue":"3","key":"5847_CR68","doi-asserted-by":"publisher","first-page":"2031","DOI":"10.1109\/COMST.2020.2986024","volume":"22","author":"WYB Lim","year":"2020","unstructured":"Lim WYB, Luong NC, Hoang DT, Jiao Y, Liang Y-C, Yang Q, Niyato D, Miao C (2020) Federated learning in mobile edge networks: A comprehensive survey. IEEE Commun Surveys Tutor 22(3):2031\u20132063","journal-title":"IEEE Commun Surveys Tutor"},{"issue":"6","key":"5847_CR69","doi-asserted-by":"publisher","DOI":"10.1088\/2057-1976\/aadbd4","volume":"4","author":"A Anusha","year":"2018","unstructured":"Anusha A, Jose J, Preejith S, Jayaraj J, Mohanasankar S (2018) Physiological signal based work stress detection using unobtrusive sensors. Biomed Phys Eng Express 4(6):065001","journal-title":"Biomed Phys Eng Express"},{"key":"5847_CR70","doi-asserted-by":"crossref","unstructured":"Zontone P, Affanni A, Bernardini R, Piras A, Rinaldo R (2019) Stress detection through electrodermal activity (eda) and electrocardiogram (ecg) analysis in car drivers. In: 2019 27th European Signal Processing Conference (EUSIPCO), pp. 1\u20135. IEEE","DOI":"10.23919\/EUSIPCO.2019.8902631"},{"issue":"4","key":"5847_CR71","doi-asserted-by":"publisher","first-page":"0230706","DOI":"10.1371\/journal.pone.0230706","volume":"15","author":"L Huang","year":"2020","unstructured":"Huang L, Yin Y, Fu Z, Zhang S, Deng H, Liu D (2020) Loadaboost: Loss-based adaboost federated machine learning with reduced computational complexity on iid and non-iid intensive care data. Plos one 15(4):0230706","journal-title":"Plos one"},{"issue":"3","key":"5847_CR72","doi-asserted-by":"publisher","first-page":"1541","DOI":"10.1109\/TAFFC.2020.3014842","volume":"13","author":"P Sarkar","year":"2020","unstructured":"Sarkar P, Etemad A (2020) Self-supervised ecg representation learning for emotion recognition. IEEE Trans Affect Comput 13(3):1541\u20131554","journal-title":"IEEE Trans Affect Comput"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05847-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-023-05847-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05847-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T10:47:38Z","timestamp":1714992458000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-023-05847-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,21]]},"references-count":72,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["5847"],"URL":"https:\/\/doi.org\/10.1007\/s11227-023-05847-3","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,21]]},"assertion":[{"value":"26 November 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 December 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}