{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:08:25Z","timestamp":1780355305416,"version":"3.54.1"},"reference-count":99,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008561","name":"Tenaga Nasional Berhard (TNB) and Universiti Tenaga Nasional through the BOLD Refresh Publication Fund","doi-asserted-by":"publisher","award":["J510050002-IC-6"],"award-info":[{"award-number":["J510050002-IC-6"]}],"id":[{"id":"10.13039\/501100008561","id-type":"DOI","asserted-by":"publisher"}]},{"name":"BOLDREFRESH2025-Centre of Excellence"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/access.2023.3267964","type":"journal-article","created":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T18:07:06Z","timestamp":1681754826000},"page":"45711-45735","source":"Crossref","is-referenced-by-count":35,"title":["A Survey of Federated Learning From Data Perspective in the Healthcare Domain: Challenges, Methods, and Future Directions"],"prefix":"10.1109","volume":"11","author":[{"given":"Zahraa Khduair","family":"Taha","sequence":"first","affiliation":[{"name":"College of Graduate Studies (COGS), Universiti Tenaga Nasional (The Energy University), Kajang, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5044-6110","authenticated-orcid":false,"given":"Chong Tak","family":"Yaw","sequence":"additional","affiliation":[{"name":"Institute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Kajang, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siaw Paw","family":"Koh","sequence":"additional","affiliation":[{"name":"Institute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Kajang, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4447-262X","authenticated-orcid":false,"given":"Sieh Kiong","family":"Tiong","sequence":"additional","affiliation":[{"name":"Institute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Kajang, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kumaran","family":"Kadirgama","sequence":"additional","affiliation":[{"name":"Advance Nano Coolant-Lubricant (ANCL), College of Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Foo","family":"Benedict","sequence":"additional","affiliation":[{"name":"Enhance Track Sdn. Bhd., Puchong, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian Ding","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Artificial Intelligence, Xiamen University Malaysia, Sepang, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yogendra AL","family":"Balasubramaniam","sequence":"additional","affiliation":[{"name":"TNB Research Sdn. Bhd., Kajang, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.101992"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108746"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.aej.2021.08.020"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2021.103735"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2021.11.028"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106679"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2021.05.013"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107338"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.3390\/ai3010008"},{"key":"ref50","article-title":"Federated learning with heterogeneous labels and models for mobile activity monitoring","author":"gudur","year":"2020","journal-title":"arXiv 2012 02539"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2022.04.021"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3161943"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2022.101726"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2022.3167994"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2019.100275"},{"key":"ref41","article-title":"Modeling global distribution for federated learning with label distribution skew","author":"sheng","year":"2022","journal-title":"arXiv 2212 08883"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2021.3108197"},{"key":"ref43","article-title":"Disentangled federated learning for tackling attributes skew via invariant aggregation and diversity transferring","author":"luo","year":"2022","journal-title":"arXiv 2206 06818"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3175149"},{"key":"ref8","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":"Appl Sci"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s41666-020-00082-4"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106775"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-01585-4"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2921977"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106854"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.07.098"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467185"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449926"},{"key":"ref34","article-title":"FedMood: Federated learning on mobile health data for mood detection","author":"xu","year":"2021","journal-title":"arXiv 2102 09342"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3184309"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00431-6"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148586"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2022.06.015"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107330"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2022.3144699"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/s10796-021-10144-6"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-69250-1"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101765"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3009406"},{"key":"ref26","article-title":"Towards federated learning on time-evolving heterogeneous data","author":"guo","year":"2021","journal-title":"arXiv 2112 13246"},{"key":"ref25","first-page":"17","article-title":"A survey of federated learning on non&#x2014;IID data","volume":"20","author":"topic","year":"2022","journal-title":"ZTE Commun"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3056919"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-022-06241-5"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","article-title":"A systematic study of the class imbalance problem in convolutional neural networks","volume":"106","author":"buda","year":"2017","journal-title":"Neural Netw"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.3390\/s23031152"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3412357"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2020.3045266"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2021.3058573"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102402"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICTC51749.2021.9441499"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2021.3075439"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2022.3196404"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2021.3083316"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3036166"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2021.3125282"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2019.2929409"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3013541"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3124599"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-016-0043-6"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1002\/sam.10080"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.3390\/fi14120377"},{"key":"ref95","article-title":"Communication efficiency in federated learning: Achievements and challenges","author":"shahid","year":"2021","journal-title":"arXiv 2107 10996"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761315"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1145\/3458864.3467681"},{"key":"ref90","first-page":"1","article-title":"Communication-efficient learning of deep networks from decentralized data","volume":"54","author":"mcmahan","year":"2017","journal-title":"Proc 20th Int Artif Intell Stat"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2020.2988604"},{"key":"ref86","first-page":"71","article-title":"Evolutionary federated learning on EEG-data","volume":"2473","author":"szegedi","year":"2019","journal-title":"Proc CEUR Workshop"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/ICCRD51685.2021.9386709"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3037474"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108672"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.04.021"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102199"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3081578"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/COMPSAC51774.2021.00070"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116109"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2021.10.016"},{"key":"ref78","article-title":"Towards federated learning at scale: System design","author":"bonawitz","year":"2019","journal-title":"arXiv 1902 01046"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904348"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3141913"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/MeditCom49071.2021.9647636"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2021.3096127"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3390\/inventions4010022"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107763"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1145\/3381006"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2022.03.030"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.02.028"},{"key":"ref68","article-title":"Collaborative federated learning for healthcare: Multi-modal COVID-19 diagnosis at the edge","author":"qayyum","year":"2021","journal-title":"arXiv 2101 07511"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3057653"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1145\/3338501.3357370"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/IWQOS52092.2021.9521339"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3098010"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/IWCMC51323.2021.9498820"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9149385"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/WF-IoT51360.2021.9595167"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3103635"},{"key":"ref61","first-page":"756","article-title":"D&#x00CF;oT: A federated self-learning anomaly detection system for IoT","author":"nguyen","year":"2019","journal-title":"Proc Int Conf Distrib Comput Syst"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10005208\/10103619.pdf?arnumber=10103619","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,12]],"date-time":"2023-06-12T18:24:28Z","timestamp":1686594268000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10103619\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":99,"URL":"https:\/\/doi.org\/10.1109\/access.2023.3267964","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}