{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T19:03:39Z","timestamp":1774206219962,"version":"3.50.1"},"reference-count":73,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council 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 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":[[2024]]},"DOI":"10.1109\/access.2024.3511430","type":"journal-article","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T19:13:36Z","timestamp":1733339616000},"page":"182496-182515","source":"Crossref","is-referenced-by-count":5,"title":["Adaptive Asynchronous Split Federated Learning for Medical Image Segmentation"],"prefix":"10.1109","volume":"12","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\/0000-0003-2018-0523","authenticated-orcid":false,"given":"Hadi","family":"Hadizadeh","sequence":"additional","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":"V.","family":"Ivan Baji\u0107","sequence":"additional","affiliation":[{"name":"School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.12419"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3042065"},{"key":"ref5","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2018.05.003"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20825"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3281832"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3545008.3545065"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2022.3205475"},{"issue":"1","key":"ref11","first-page":"2","article-title":"Elfish: Resource-aware federated learning on heterogeneous edge devices","volume":"2","author":"Xu","year":"2019","journal-title":"Ratio"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2023.3310103"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3118435"},{"key":"ref14","first-page":"3581","article-title":"Federated learning with buffered asynchronous aggregation","volume-title":"Proc. AISTATS","author":"Nguyen"},{"key":"ref15","first-page":"706","article-title":"Asynchronous personalized federated learning with irregular clients","volume-title":"Proc. ACML","author":"Ma"},{"key":"ref16","article-title":"PersA-FL: Personalized asynchronous federated learning","author":"Taha Toghani","year":"2022","journal-title":"arXiv:2210.01176"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00535"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2023.100595"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/BigData50022.2020.9378161"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.jpdc.2022.07.009","article-title":"AMBLE: Adjusting mini-batch and local epoch for federated learning with heterogeneous devices","volume":"170","author":"Park","year":"2022","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3096846"},{"key":"ref22","article-title":"Asynchronous federated optimization","author":"Xie","year":"2019","journal-title":"arXiv:1903.03934"},{"key":"ref23","article-title":"Learning rate adaptation for federated and differentially private learning","author":"Koskela","year":"2018","journal-title":"arXiv:1809.03832"},{"key":"ref24","first-page":"1","article-title":"Local epochs inefficiency caused by device heterogeneity in federated learning","volume":"2022","author":"Zeng","year":"2022","journal-title":"Wireless Commun. Mobile Comput."},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3075291"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3299573"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3593434.3593438"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-023-10011-4"},{"key":"ref29","article-title":"Client selection in federated learning: Convergence analysis and power-of-choice selection strategies","author":"Jee Cho","year":"2020","journal-title":"arXiv:2010.01243"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3195073"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3161943"},{"key":"ref32","first-page":"1","article-title":"A snapshot of the frontiers of client selection in federated learning","volume":"abs\/2210.04607","author":"N\u00e9meth","year":"2022","journal-title":"JMLR"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3369583.3392686"},{"key":"ref34","first-page":"3407","article-title":"Clustered sampling: Low-variance and improved representativity for clients selection in federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Fraboni"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2022.3181678"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2019.00099"},{"key":"ref37","article-title":"Communication efficiency in federated learning: Achievements and challenges","author":"Shahid","year":"2021","journal-title":"arXiv:2107.10996"},{"key":"ref38","first-page":"22802","article-title":"Communication-efficient adaptive federated learning","volume-title":"Proc. ICML","author":"Wang"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2024789118"},{"key":"ref40","first-page":"3973","article-title":"FedBOOST: A communication-efficient algorithm for federated learning","volume-title":"Proc. ICML","author":"Hamer"},{"key":"ref41","article-title":"Federated learning: Strategies for improving communication efficiency","author":"Kone\u010dn\u00fd","year":"2016","journal-title":"arXiv:1610.05492"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3489517.3530394"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2022.3140788"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.2974748"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2022.3152445"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2022.3153338"},{"key":"ref47","article-title":"Loss tolerant federated learning","author":"Zhou","year":"2021","journal-title":"arXiv:2105.03591"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP49357.2023.10095067"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-47401-9_35"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3134647"},{"key":"ref51","first-page":"10351","article-title":"Towards understanding biased client selection in federated learning","volume-title":"Proc. AISTATS","volume":"151","author":"Jee Cho"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/springerreference_63652"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2977050"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP42928.2021.9506372"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2010.5434378"},{"key":"ref56","volume-title":"Numpy Api Reference Manual","year":"2023"},{"key":"ref57","volume-title":"Optimization and Root Finding (scipy.optimize)\u2014Scipy V1.11.4 Manual","year":"2023"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/MMSP.2019.8901697"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.161"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2017.2759665"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"ref62","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. ICLR","author":"Kingma"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI53787.2023.10230484"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.2307\/2331554"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-4380-9_6"},{"key":"ref66","article-title":"Auto-FedAvg: Learnable federated averaging for multi-institutional medical image segmentation","author":"Xia","year":"2021","journal-title":"arXiv:2104.10195"},{"key":"ref67","article-title":"Federated noisy client learning","author":"Li","year":"2021","journal-title":"arXiv:2106.13239"},{"key":"ref68","article-title":"On the convergence of FedAvg on non-IID data","author":"Li","year":"2019","journal-title":"arXiv:1907.02189"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904348"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3036952"},{"key":"ref71","first-page":"1","article-title":"Accelerating federated learning with split learning on locally generated losses","volume-title":"Proc. ICML","author":"Han"},{"key":"ref72","article-title":"When MiniBatch SGD meets SplitFed learning: Convergence analysis and performance evaluation","author":"Huang","year":"2023","journal-title":"arXiv:2308.11953"},{"key":"ref73","volume-title":"Optimization By Vector Space Methods","author":"Luenberger","year":"1997"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10380310\/10776986.pdf?arnumber=10776986","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T06:44:18Z","timestamp":1733985858000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10776986\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":73,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3511430","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}