{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T22:14:34Z","timestamp":1780611274228,"version":"3.54.1"},"reference-count":173,"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-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3422211","type":"journal-article","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T19:35:13Z","timestamp":1719948913000},"page":"101016-101052","source":"Crossref","is-referenced-by-count":30,"title":["Decentralized and Distributed Learning for AIoT: A Comprehensive Review, Emerging Challenges, and Opportunities"],"prefix":"10.1109","volume":"12","author":[{"given":"Hanyue","family":"Xu","sequence":"first","affiliation":[{"name":"XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong–Liverpool University, Taicang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8071-9044","authenticated-orcid":false,"given":"Kah Phooi","family":"Seng","sequence":"additional","affiliation":[{"name":"XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong–Liverpool University, Taicang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2402-7529","authenticated-orcid":false,"given":"Li Minn","family":"Ang","sequence":"additional","affiliation":[{"name":"School of Science, Technology and Engineering, University of the Sunshine Coast, Petrie, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeremy","family":"Smith","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/iwqos57198.2023.10188781"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/wf-iot48130.2020.9221208"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/jiot.2021.3109435"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11101548"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/tvt.2022.3217963"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/infocom41043.2020.9155494"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3030072"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2021.3075439"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2023.3315746"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3160699"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2022.3200740"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3323932"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2021.3086014"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3088875"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3133819"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3234761"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3281832"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3390\/s22165983"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3284976"},{"key":"ref20","first-page":"6453","article-title":"Federated principal component analysis","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Grammenos"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3326981"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP43922.2022.9747220"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2021.3099448"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymeth.2022.03.005"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2023.100784"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3046509"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/FMEC49853.2020.9144705"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2022.11.012"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/SBAC-PAD.2019.00035"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3080397"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-021-04176-7"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/1536414.1536440"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/tcbb.2018.2833463"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2015.08.016"},{"key":"ref35","first-page":"201","article-title":"CryptoNets: Applying neural networks to encrypted data with high throughput and accuracy","volume-title":"Proc. 33rd Int. Conf. Mach. Learn.","volume":"48","author":"Dowlin"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-17533-1_36"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/icde.2006.1"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.joems.2012.08.004"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2019.10.009"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.29012\/jpc.v4i1.612"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2016.2607691"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2013.2259911"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.2966251"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/tsc.2020.3008957"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3151115"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098152"},{"key":"ref47","first-page":"323","article-title":"Global analytics in the face of bandwidth and regulatory constraints","volume-title":"Proc. 12th USENIX Conf. Netw. Syst. Design Implement. (NSDI)","author":"Vulimiri"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2019.2896894"},{"key":"ref49","article-title":"Private federated learning in Gboard","author":"Zhang","year":"2023","journal-title":"arXiv:2306.14793"},{"key":"ref50","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"Brendan McMahan","year":"2016","journal-title":"arXiv:1602.05629"},{"key":"ref51","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. 37th Int. Conf. Mach. Learn. (ICML)","volume":"119","author":"Karimireddy"},{"key":"ref52","article-title":"Asynchronous federated optimization","author":"Xie","year":"2019","journal-title":"arXiv:1903.03934"},{"key":"ref53","article-title":"Measuring the effects of non-identical data distribution for federated visual classification","author":"Harry Hsu","year":"2019","journal-title":"arXiv:1909.06335"},{"key":"ref54","article-title":"Federated optimization in heterogeneous networks","author":"Li","year":"2018","journal-title":"arXiv:1812.06127"},{"key":"ref55","article-title":"Tackling the objective inconsistency problem in heterogeneous federated optimization","author":"Wang","year":"2020","journal-title":"arXiv:2007.07481"},{"key":"ref56","first-page":"3973","article-title":"FedBoost: Communication-efficient algorithms for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hamer"},{"key":"ref57","article-title":"FedBR: Improving federated learning on heterogeneous data via local learning bias reduction","author":"Guo","year":"2022","journal-title":"arXiv:2205.13462"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/OJCS.2020.2993259"},{"key":"ref59","article-title":"Three approaches for personalization with applications to federated learning","author":"Mansour","year":"2020","journal-title":"arXiv:2002.10619"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/JSYST.2022.3230425"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/tpds.2021.3098467"},{"key":"ref62","article-title":"Personalized federated learning: A meta-learning approach","author":"Fallah","year":"2020","journal-title":"arXiv:2002.07948"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/access.2023.3294812"},{"key":"ref64","article-title":"Exploiting shared representations for personalized federated learning","author":"Collins","year":"2021","journal-title":"arXiv:2102.07078"},{"key":"ref65","article-title":"Federated learning of a mixture of global and local models","author":"Hanzely","year":"2020","journal-title":"arXiv:2002.05516"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2018.05.003"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-70604-3_4"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/GCWkshps58843.2023.10465123"},{"key":"ref69","article-title":"SplitNN-driven vertical partitioning","author":"Ceballos","year":"2020","journal-title":"arXiv:2008.04137"},{"key":"ref70","article-title":"Split learning for health: Distributed deep learning without sharing raw patient data","author":"Vepakomma","year":"2018","journal-title":"arXiv:1812.00564"},{"key":"ref71","article-title":"Detailed comparison of communication efficiency of split learning and federated learning","author":"Singh","year":"2019","journal-title":"arXiv:1909.09145"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/srds51746.2020.00017"},{"key":"ref73","article-title":"AdaSplit: Adaptive trade-offs for resource-constrained distributed deep learning","author":"Chopra","year":"2021","journal-title":"arXiv:2112.01637"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/globecom46510.2021.9685493"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/519"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/MLSP55214.2022.9943507"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/sips52927.2021.00022"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/infocom42981.2021.9488776"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.3390\/mps5040060"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20825"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-80432-9_34"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/tc.2021.3135752"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/tbdata.2023.3280405"},{"key":"ref84","article-title":"Federated split vision transformer for COVID-19 CXR diagnosis using task-agnostic training","author":"Park","year":"2021","journal-title":"arXiv:2111.01338"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1145\/3510033"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/iwqos54832.2022.9812907"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/icws60048.2023.00084"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2022.109380"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772758"},{"key":"ref90","article-title":"FedLite: A scalable approach for federated learning on resource-constrained clients","author":"Wang","year":"2022","journal-title":"arXiv:2201.11865"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1109\/icdcs57875.2023.00096"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/tcomm.2023.3277878"},{"key":"ref93","first-page":"1","article-title":"Accelerating federated learning with split learning on locally generated losses","volume-title":"Proc. ICML","author":"Han"},{"key":"ref94","article-title":"Decoupled greedy learning of CNNs","author":"Belilovsky","year":"2019","journal-title":"arXiv:1901.08164"},{"key":"ref95","article-title":"Accelerating gossip SGD with periodic global averaging","author":"Chen","year":"2021","journal-title":"arXiv:2105.09080"},{"key":"ref96","article-title":"Decentralized stochastic optimization and gossip algorithms with compressed communication","author":"Koloskova","year":"2019","journal-title":"arXiv:1902.00340"},{"key":"ref97","article-title":"A unified theory of decentralized SGD with changing topology and local updates","author":"Koloskova","year":"2020","journal-title":"arXiv:2003.10422"},{"key":"ref98","article-title":"BrainTorrent: A peer-to-peer environment for decentralized federated learning","author":"Guha Roy","year":"2019","journal-title":"arXiv:1905.06731"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1109\/jiot.2019.2940820"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1109\/jiot.2021.3138693"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1109\/jiot.2023.3267112"},{"key":"ref102","article-title":"ModelChain: Decentralized privacy-preserving healthcare predictive modeling framework on private blockchain networks","author":"Kuo","year":"2018","journal-title":"arXiv:1802.01746"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2021.12.272"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.2858"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-22496-7_5"},{"key":"ref106","first-page":"1","article-title":"Fully decentralized federated learning","volume-title":"Proc. NeurIPS","author":"Lalitha"},{"key":"ref107","article-title":"Decentralized federated learning: A segmented gossip approach","author":"Hu","year":"2019","journal-title":"arXiv:1908.07782"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03583-3"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-022-01768-5"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1016\/j.jii.2023.100481"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.3390\/s22166252"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13203166"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2012.12.002"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1109\/AsiaJCIS50894.2020.00031"},{"key":"ref115","article-title":"Boosting privately: Privacy-preserving federated extreme boosting for mobile crowdsensing","author":"Liu","year":"2019","journal-title":"arXiv:1907.10218"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5895"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2018.8486352"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1109\/mis.2021.3082561"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3169502"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63833-7_47"},{"key":"ref122","article-title":"Heterogeneity for the win: One-shot federated clustering","author":"Kurian Dennis","year":"2021","journal-title":"arXiv:2103.00697"},{"key":"ref123","article-title":"LEAF: A benchmark for federated settings","author":"Caldas","year":"2018","journal-title":"arXiv:1812.01097"},{"key":"ref124","article-title":"Orchestra: Unsupervised federated learning via globally consistent clustering","author":"Lubana","year":"2022","journal-title":"arXiv:2205.11506"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2020.3015958"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-15184-5"},{"key":"ref127","article-title":"LSTMSPLIT: Effective SPLIT learning based LSTM on sequential time-series data","author":"Jiang","year":"2022","journal-title":"arXiv:2203.04305"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1109\/jbhi.2023.3281977"},{"key":"ref129","article-title":"Group knowledge transfer: Federated learning of large CNNs at the edge","author":"He","year":"2020","journal-title":"arXiv:2007.14513"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1109\/jiot.2022.3172114"},{"key":"ref131","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/324"},{"key":"ref132","article-title":"FedGraphNN: A federated learning system and benchmark for graph neural networks","author":"He","year":"2021","journal-title":"arXiv:2104.07145"},{"key":"ref133","article-title":"Federated graph classification over non-IID graphs","author":"Xie","year":"2021","journal-title":"arXiv:2106.13423"},{"key":"ref134","article-title":"Subgraph federated learning with missing neighbor generation","author":"Zhang","year":"2021","journal-title":"arXiv:2106.13430"},{"key":"ref135","article-title":"Personalized subgraph federated learning","author":"Baek","year":"2022","journal-title":"arXiv:2206.10206"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-30714-9"},{"key":"ref137","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467371"},{"key":"ref138","first-page":"1","article-title":"Decentralized federated graph neural networks","volume-title":"Proc. IJCAI","author":"Pei"},{"key":"ref139","doi-asserted-by":"publisher","DOI":"10.3390\/s23229226"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.1109\/jiot.2021.3086910"},{"key":"ref141","doi-asserted-by":"publisher","DOI":"10.1109\/wf-iot48130.2020.9221089"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.3390\/s20051359"},{"key":"ref143","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108327"},{"key":"ref144","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3176469"},{"key":"ref145","doi-asserted-by":"publisher","DOI":"10.1109\/ICSESS52187.2021.9522334"},{"key":"ref146","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3118352"},{"key":"ref147","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2945367"},{"key":"ref148","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48506.2021.9560791"},{"key":"ref149","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM48099.2022.10000980"},{"key":"ref150","first-page":"1","article-title":"A federated learning-based crop yield prediction for agricultural production risk management","volume-title":"Proc. IEEE Delhi Sect. Conf. (DELCON)","author":"Manoj"},{"key":"ref151","doi-asserted-by":"publisher","DOI":"10.1016\/j.atech.2023.100277"},{"key":"ref152","doi-asserted-by":"publisher","DOI":"10.1109\/tim.2022.3201937"},{"key":"ref153","doi-asserted-by":"publisher","DOI":"10.1109\/tvt.2022.3232603"},{"key":"ref154","doi-asserted-by":"publisher","DOI":"10.1109\/tvt.2022.3178808"},{"key":"ref155","doi-asserted-by":"publisher","DOI":"10.1109\/itsc45102.2020.9294453"},{"key":"ref156","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3095506"},{"key":"ref157","doi-asserted-by":"publisher","DOI":"10.3390\/en15176241"},{"key":"ref158","doi-asserted-by":"publisher","DOI":"10.1109\/OJCOMS.2021.3072003"},{"key":"ref159","doi-asserted-by":"publisher","DOI":"10.1109\/PIMRC56721.2023.10294036"},{"key":"ref160","doi-asserted-by":"publisher","DOI":"10.1109\/IoTDI49375.2020.00017"},{"key":"ref161","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3035451"},{"key":"ref162","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3228727"},{"key":"ref163","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2022.3175945"},{"key":"ref164","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3141913"},{"key":"ref165","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-05615-y"},{"key":"ref166","doi-asserted-by":"publisher","DOI":"10.3390\/s21113758"},{"key":"ref167","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.3012952"},{"key":"ref168","doi-asserted-by":"publisher","DOI":"10.1109\/RTSS46320.2019.00043"},{"key":"ref169","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2023.103249"},{"key":"ref170","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599825"},{"key":"ref171","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6824"},{"key":"ref172","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.2979147"},{"key":"ref173","doi-asserted-by":"publisher","DOI":"10.1109\/MVT.2020.3019650"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10380310\/10580879.pdf?arnumber=10580879","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T17:35:27Z","timestamp":1721928927000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10580879\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":173,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3422211","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}