{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T16:22:27Z","timestamp":1784132547649,"version":"3.55.0"},"reference-count":36,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T00:00:00Z","timestamp":1687132800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T00:00:00Z","timestamp":1687132800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172241"],"award-info":[{"award-number":["62172241"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006606","name":"Natural Science Foundation of Tianjin","doi-asserted-by":"publisher","award":["20JCZDJC00610"],"award-info":[{"award-number":["20JCZDJC00610"]}],"id":[{"id":"10.13039\/501100006606","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"U.S. National Science Foundation","doi-asserted-by":"publisher","award":["CNS-2047719,CNS-2225949"],"award-info":[{"award-number":["CNS-2047719,CNS-2225949"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,19]]},"DOI":"10.1109\/iwqos57198.2023.10188789","type":"proceedings-article","created":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T17:21:00Z","timestamp":1690478460000},"page":"1-10","source":"Crossref","is-referenced-by-count":10,"title":["When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework"],"prefix":"10.1109","author":[{"given":"Xinjing","family":"Yuan","sequence":"first","affiliation":[{"name":"Institute of Systems and Networks, College of Computer Science, Nankai University,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingjun","family":"Pu","sequence":"additional","affiliation":[{"name":"Institute of Systems and Networks, College of Computer Science, Nankai University,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Jiao","sequence":"additional","affiliation":[{"name":"University of Oregon,Department of Computer Science,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Tianjin University,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meijuan","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Systems and Networks, College of Computer Science, Nankai University,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingdong","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Systems and Networks, College of Computer Science, Nankai University,China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","article-title":"Federated accelerated stochastic gradient descent","author":"yuan","year":"0","journal-title":"NeurIPS"},{"key":"ref35","article-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications","author":"howard","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref12","article-title":"On the Convergence of FedAvg on Non-IID Data","author":"li","year":"2020","journal-title":"ICLRE"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2022.3177569"},{"key":"ref14","article-title":"Split learning for health: Distributed deep learning without sharing raw patient data","author":"vepakomma","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/s00453-006-1210-5"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-79995-2"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2023.3280405"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10575-8_11"},{"key":"ref10","article-title":"Efficient split-mix federated learning for on-demand and in-situ customization","author":"hong","year":"2022","journal-title":"ICLRE"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/0022-0000(88)90003-7"},{"key":"ref2","year":"0","journal-title":"Huawei Industry Report Communications Network 2030"},{"key":"ref1","year":"0","journal-title":"ITU-T Technical Report Representative use cases and key network requirements for Network 2030"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2021.3090430"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2986024"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM46510.2021.9685045"},{"key":"ref18","article-title":"Split Learning over Wireless Networks : Parallel Design and Resource Management","author":"wu","year":"2022","journal-title":"ArXiv Preprint"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM38437.2019.9013404"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM46510.2021.9685971"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450051"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2981338"},{"key":"ref20","article-title":"Evaluation and optimization of distributed machine learning techniques for internet of things","author":"gao","year":"2021","journal-title":"IEEE TC"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2021.3101460"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2017.2745201"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.005.2100440"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.13.7.492"},{"key":"ref29","year":"0","journal-title":"Online technical report"},{"key":"ref8","article-title":"Accelerating federated learning with split learning on locally generated losses","author":"han","year":"2021","journal-title":"ICML"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20825"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512153"},{"key":"ref4","article-title":"Findings and Recommendations for Compute First Networking","author":"crowcroft","year":"2021","journal-title":"Dagstuhl Reports"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3357150.3357395"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3458336.3465301"},{"key":"ref5","year":"0","journal-title":"ITU-T Technical Report Computing power network - Framework and architecture"}],"event":{"name":"2023 IEEE\/ACM 31st International Symposium on Quality of Service (IWQoS)","location":"Orlando, FL, USA","start":{"date-parts":[[2023,6,19]]},"end":{"date-parts":[[2023,6,21]]}},"container-title":["2023 IEEE\/ACM 31st International Symposium on Quality of Service (IWQoS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10188660\/10188690\/10188789.pdf?arnumber=10188789","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T17:38:47Z","timestamp":1692034727000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10188789\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,19]]},"references-count":36,"URL":"https:\/\/doi.org\/10.1109\/iwqos57198.2023.10188789","relation":{},"subject":[],"published":{"date-parts":[[2023,6,19]]}}}