{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T19:04:41Z","timestamp":1774206281538,"version":"3.50.1"},"reference-count":182,"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\/501100000038","name":"Natural Sciences and Engineering Research Council (NSERC) of Canada","doi-asserted-by":"publisher","award":["RGPIN-2021-02485"],"award-info":[{"award-number":["RGPIN-2021-02485"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council (NSERC) of Canada","doi-asserted-by":"publisher","award":["RGPAS-2021-00038"],"award-info":[{"award-number":["RGPAS-2021-00038"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/access.2023.3281832","type":"journal-article","created":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T17:33:51Z","timestamp":1685640831000},"page":"54188-54209","source":"Crossref","is-referenced-by-count":37,"title":["Decentralized Learning in Healthcare: A Review of Emerging Techniques"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1608-8196","authenticated-orcid":false,"given":"Chamani","family":"Shiranthika","sequence":"first","affiliation":[{"name":"School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7507-9986","authenticated-orcid":false,"given":"Parvaneh","family":"Saeedi","sequence":"additional","affiliation":[{"name":"School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3154-5743","authenticated-orcid":false,"given":"Ivan V.","family":"Baji\u0107","sequence":"additional","affiliation":[{"name":"School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada"}]}],"member":"263","reference":[{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098118"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W19-5030"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2014.2377694"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-11723-8_9"},{"key":"ref53","article-title":"Split learning over wireless networks: Parallel design and resource management","author":"wu","year":"2022","journal-title":"arXiv 2204 08119"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512153"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6121"},{"key":"ref168","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM48099.2022.10001205"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICC45855.2022.9882277"},{"key":"ref169","article-title":"Communication-efficient federated learning with acceleration of global momentum","author":"kim","year":"2022","journal-title":"arXiv 2201 03172"},{"key":"ref170","article-title":"Split HE: Fast secure inference combining split learning and homomorphic encryption","author":"pereteanu","year":"2022","journal-title":"arXiv 2202 13351"},{"key":"ref177","doi-asserted-by":"publisher","DOI":"10.1145\/3560905.3568302"},{"key":"ref178","doi-asserted-by":"publisher","DOI":"10.1007\/s11265-022-01781-4"},{"key":"ref51","article-title":"FedLite: A scalable approach for federated learning on resource-constrained clients","author":"wang","year":"2022","journal-title":"arXiv 2201 11865"},{"key":"ref175","doi-asserted-by":"publisher","DOI":"10.1145\/3460120.3485259"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.003.2100964"},{"key":"ref176","doi-asserted-by":"publisher","DOI":"10.1145\/3559613.3563201"},{"key":"ref173","article-title":"Computational privacy with split learning: Benchmarking of algorithmic defenses against reconstruction attacks","author":"zhang","year":"2021"},{"key":"ref174","doi-asserted-by":"publisher","DOI":"10.1145\/3559613.3563198"},{"key":"ref171","first-page":"27","article-title":"Label leakage and protection in two-party split learning","author":"li","year":"2021","journal-title":"Proc ICLR"},{"key":"ref172","article-title":"Differentially private label protection in split learning","author":"yang","year":"2022","journal-title":"arXiv 2203 02073"},{"key":"ref46","first-page":"7","article-title":"A novel approach to simultaneously improve privacy, efficiency and reliability of federated DNN learning","author":"gu","year":"2021","journal-title":"Proc Int Workshop Federated Transf Learn Data Sparsity Confidentiality Conjunct With (IJCAI)"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICOIN48656.2020.9016486"},{"key":"ref48","article-title":"AdaSplit: Adaptive trade-offs for resource-constrained distributed deep learning","author":"chopra","year":"2021","journal-title":"arXiv 2112 01637"},{"key":"ref47","first-page":"9","article-title":"Pyvertical: A vertical federated learning framework for multi-headed SplitNN","author":"romanini","year":"2021","journal-title":"Proc ICLR Workshop DPML"},{"key":"ref42","article-title":"Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performance","author":"joshi","year":"2021","journal-title":"arXiv 2109 09246"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.3390\/mps5040060"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/3386367.3431678"},{"key":"ref179","doi-asserted-by":"publisher","DOI":"10.1145\/3533708"},{"key":"ref43","article-title":"Server-side local gradient averaging and learning rate acceleration for scalable split learning","author":"pal","year":"2021","journal-title":"arXiv 2112 05929"},{"key":"ref49","first-page":"12","article-title":"Accelerating federated learning with split learning on locally generated losses","author":"han","year":"2021","journal-title":"Proc Int Workshop Federated Learn User Privacy Data Confidentiality Conjunct With (ICML)"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2018.05.003"},{"key":"ref180","doi-asserted-by":"publisher","DOI":"10.1108\/IJWIS-04-2022-0080"},{"key":"ref7","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Proc AISTATS"},{"key":"ref181","doi-asserted-by":"publisher","DOI":"10.1109\/CogMI56440.2022.00013"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1155\/2007\/13801"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-03549-4_20"},{"key":"ref5","first-page":"34","article-title":"Scalable private learning with PATE","author":"papernot","year":"2018","journal-title":"Proc ICLR"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-30695-9"},{"key":"ref101","author":"litjens","year":"2017","journal-title":"SPIE-AAPM PROSTATEx Challenge Data Version Number 2 Type Dataset"},{"key":"ref40","article-title":"Comparison of privacy-preserving distributed deep learning methods in healthcare","author":"gawali","year":"2020","journal-title":"arXiv 2012 12591"},{"key":"ref182","first-page":"19184","article-title":"Federated hyperparameter tuning: Challenges, baselines, and connections to weight-sharing","volume":"34","author":"khodak","year":"2021","journal-title":"Proc NeurIPS"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2022.109380"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2021.3135752"},{"key":"ref37","article-title":"Detailed comparison of communication efficiency of split learning and federated learning","author":"singh","year":"2019","journal-title":"arXiv 1909 09145"},{"key":"ref36","first-page":"1866","article-title":"SplitNet: Learning to semantically split deep networks for parameter reduction and model parallelization","volume":"70","author":"kim","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn (ICML)"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2211477"},{"key":"ref148","first-page":"22","article-title":"Virtual homogeneity learning: Defending against data heterogeneity in federated learning","author":"tang","year":"2022","journal-title":"Proc PMLR"},{"key":"ref30","article-title":"SplitNN-driven vertical partitioning","author":"ceballos","year":"2020","journal-title":"arXiv 2008 04137"},{"key":"ref149","article-title":"FedBN: Federated learning on non-IID features via local batch normalization","author":"li","year":"2021","journal-title":"arXiv 2102 07623"},{"key":"ref33","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref146","article-title":"FedKL: Tackling data heterogeneity in federated reinforcement learning by penalizing KL divergence","author":"xie","year":"2022","journal-title":"arXiv 2204 08125"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet large scale visual recognition challenge","volume":"115","author":"russakovsky","year":"2015","journal-title":"Int J Comput Vis"},{"key":"ref147","first-page":"4","article-title":"Towards taming the resource and data heterogeneity in federated learning","author":"chai","year":"2019","journal-title":"Proc OpML"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20825"},{"key":"ref38","article-title":"SplitFed: When federated learning meets split learning","author":"thapa","year":"2020","journal-title":"arXiv 2004 12088"},{"key":"ref155","first-page":"12","article-title":"Personalized federated learning with Moreau envelopes","author":"dinh","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref156","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2022.3169117"},{"key":"ref153","article-title":"MetaFed: Federated learning among federations with cyclic knowledge distillation for personalized healthcare","author":"chen","year":"2022","journal-title":"arXiv 2206 08516"},{"key":"ref154","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/223"},{"key":"ref151","article-title":"Federated learning with personalization layers","author":"arivazhagan","year":"2019","journal-title":"arXiv 1912 00818"},{"key":"ref152","first-page":"12","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach","author":"fallah","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref150","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2022.3177197"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101765"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3057653"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s10796-021-10144-6"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3037207"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00323-1"},{"key":"ref159","first-page":"6357","article-title":"Ditto: Fair and robust federated learning through personalization","author":"li","year":"2021","journal-title":"Proc PMLR"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1201\/b12207"},{"key":"ref157","first-page":"1","article-title":"Federated multi-task learning","volume":"30","author":"smith","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref21","article-title":"Federated learning for medical applications: A taxonomy, current trends, challenges, and future research directions","author":"rauniyar","year":"2022","journal-title":"arXiv 2208 03392"},{"key":"ref158","article-title":"Variational federated multi-task learning","author":"corinzia","year":"2019","journal-title":"arXiv 1906 06268"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2018.01.007"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2953131"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9413943"},{"key":"ref166","article-title":"Auto-FedAvg: Learnable federated averaging for multi-institutional medical image segmentation","author":"xia","year":"2021","journal-title":"arXiv 2104 10195"},{"key":"ref167","doi-asserted-by":"publisher","DOI":"10.3390\/app12125806"},{"key":"ref164","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-60548-3_16"},{"key":"ref165","first-page":"9","article-title":"WAFFLE: Weighted averaging for personalized federated learning","author":"beaussart","year":"2021","journal-title":"Proc NeurIPS Workshop New Frontiers Federated Learn (NFFL)"},{"key":"ref162","first-page":"27","article-title":"Fair resource allocation in federated learning","author":"li","year":"2019","journal-title":"Proc ICLR"},{"key":"ref163","article-title":"Hierarchically fair federated learning","author":"zhang","year":"2020","journal-title":"arXiv 2004 10386"},{"key":"ref160","article-title":"Adaptive personalized federated learning","author":"deng","year":"2020","journal-title":"arXiv 2003 13461"},{"key":"ref161","first-page":"4615","article-title":"Agnostic federated learning","author":"mohri","year":"2019","journal-title":"Proc PMLR"},{"key":"ref13","article-title":"Communication efficiency in federated learning: Achievements and challenges","author":"shahid","year":"2021","journal-title":"arXiv 2107 10996"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2986024"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref128","article-title":"COVID-CT-dataset: A CT scan dataset about COVID-19","author":"yang","year":"2020","journal-title":"arXiv 2003 13865"},{"key":"ref14","article-title":"Threats to federated learning: A survey","author":"lyu","year":"2020","journal-title":"arXiv 2003 02133"},{"key":"ref129","doi-asserted-by":"crossref","first-page":"1534","DOI":"10.1038\/s41598-022-05615-y","article-title":"Feasibility study of multi-site split learning for privacy-preserving medical systems under data imbalance constraints in COVID-19, X-ray, and cholesterol dataset","volume":"12","author":"ha","year":"2022","journal-title":"Sci Rep"},{"key":"ref97","year":"2023","journal-title":"Diabetic Retinopathy Detection"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3108455"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1145\/3163080.3163087"},{"key":"ref127","article-title":"MURA: Large dataset for abnormality detection in musculoskeletal radiographs","author":"rajpurkar","year":"2017","journal-title":"arXiv 1712 06957"},{"key":"ref11","article-title":"Towards utilizing unlabeled data in federated learning: A survey and prospective","author":"jin","year":"2020","journal-title":"arXiv 2002 11545"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87199-4_34"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-18523-6_5"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/WorldS450073.2020.9210355"},{"key":"ref98","article-title":"Federated learning with research prototypes for multi-center MRI-based detection of prostate cancer with diverse histopathology","author":"rajagopal","year":"2022","journal-title":"arXiv 2206 05617"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3185956"},{"key":"ref17","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":"ref16","doi-asserted-by":"publisher","DOI":"10.18280\/isi.270117"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/s41666-020-00082-4"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-08999-2_1"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.5566\/ias.1155"},{"key":"ref133","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN55064.2022.9891964"},{"key":"ref92","first-page":"207","article-title":"EyePACS: An open source clinical communication system for eye care","author":"cuadros","year":"2004","journal-title":"Proc Medinfo"},{"key":"ref134","article-title":"Federated noisy client learning","author":"tam","year":"2021","journal-title":"arXiv 2106 13239"},{"key":"ref95","year":"2019","journal-title":"APTOS"},{"key":"ref131","article-title":"COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images","volume":"10","author":"wang","year":"2020","journal-title":"Sci Rep"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.3390\/data3030025"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1145\/3083187.3083212"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467185"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1109\/SoutheastCon48659.2022.9764031"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1016\/j.xops.2021.100069"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107318"},{"key":"ref139","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3131852"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare10040729"},{"key":"ref137","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2022.3151466"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2019.104863"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1145\/3517821"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-60548-3_18"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412599"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2017.2731873"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00983"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/ICCWAMTIP53232.2021.9674116"},{"key":"ref144","article-title":"Federated learning with non-IID data","author":"zhao","year":"2018","journal-title":"arXiv 1806 00582"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2020.106221"},{"key":"ref145","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17291"},{"key":"ref84","article-title":"FedMix: Mixed supervised federated learning for medical image segmentation","author":"wicaksana","year":"2022","journal-title":"arXiv 2205 01840"},{"key":"ref142","first-page":"26","article-title":"On the convergence of FedAvg on non-IID","author":"li","year":"2020","journal-title":"Proc ICLR"},{"key":"ref83","article-title":"Privacy-preserving constrained domain generalization for medical image classification","author":"xing tian","year":"2021","journal-title":"arXiv 2105 08511"},{"key":"ref143","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref141","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"li","year":"2020","journal-title":"Proc Mach Learn Syst"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2018.2824327"},{"key":"ref79","article-title":"The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions","volume":"5","author":"tschandl","year":"2018","journal-title":"Data Science Journal"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocz199"},{"key":"ref78","article-title":"Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (ISIC)","author":"codella","year":"2019","journal-title":"arXiv 1902 03368"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103138"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-42051-1_16"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0255397"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocaa017"},{"key":"ref104","article-title":"Federated cross learning for medical image segmentation","author":"xu","year":"2022","journal-title":"arXiv 2204 02450"},{"key":"ref74","first-page":"475","article-title":"Two public chest X-ray datasets for computer-aided screening of pulmonary diseases","volume":"4","author":"jaeger","year":"2014","journal-title":"Quant Imaging Med Surg"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-90874-4_10"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87199-4_32"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2013.12.002"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.369"},{"key":"ref103","year":"2013","journal-title":"NCI-ISBI"},{"key":"ref2","author":"meehan","year":"2019","journal-title":"Data Privacy Will Be the Most Important Issue in the Next Decade"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2788044"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00431-6"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2020.2988604"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3029445"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.2196\/23728"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107330"},{"key":"ref72","year":"2023","journal-title":"Coronacases org"},{"key":"ref110","article-title":"Learn electronic health records by fully decentralized federated learning","author":"lu","year":"2019","journal-title":"arXiv 1912 01792"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32692-0_16"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301590"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-60548-3_20"},{"key":"ref117","article-title":"Split learning for collaborative deep learning in healthcare","author":"poirot","year":"2019","journal-title":"arXiv 1912 12115"},{"key":"ref69","author":"aerts","year":"2019","journal-title":"Data From NSCLC-Radiomics Version Number 4 Type Dataset"},{"key":"ref118","article-title":"Split learning in health care","author":"poirot","year":"2020"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2017.04.004"},{"key":"ref115","article-title":"Federated uncertainty-aware learning for distributed hospital EHR data","author":"boughorbel","year":"2019","journal-title":"arXiv 1910 12191"},{"key":"ref63","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s12021-017-9348-7","article-title":"A novel public MR image dataset of multiple sclerosis patients with lesion segmentations based on multi-rater consensus","volume":"16","author":"galimzianova","year":"2018","journal-title":"Neuroinformatics"},{"key":"ref116","article-title":"Split learning for health: Distributed deep learning without sharing raw patient data","author":"vepakomma","year":"2018","journal-title":"arXiv 1812 00564"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1038\/mp.2013.78"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC44109.2020.9175344"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759317"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103291"},{"key":"ref60","article-title":"FedDis: Disentangled federated learning for unsupervised brain pathology segmentation","author":"bercea","year":"2021","journal-title":"arXiv 2103 03705"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-020-0495-6"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1145\/3320269.3384740"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2016.10.006"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1109\/ICICIS52592.2021.9694163"},{"key":"ref61","article-title":"OASIS-3: Longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease","author":"lamontagne","year":"2019","journal-title":"medRxiv"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1109\/ICHI54592.2022.00048"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10005208\/10141615.pdf?arnumber=10141615","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T13:53:53Z","timestamp":1686318833000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10141615\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":182,"URL":"https:\/\/doi.org\/10.1109\/access.2023.3281832","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}