{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T18:52:47Z","timestamp":1774896767612,"version":"3.50.1"},"reference-count":36,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"14","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["62072485"],"award-info":[{"award-number":["62072485"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Science and Technology Research Foundation","award":["JCYJ20230807110802005"],"award-info":[{"award-number":["JCYJ20230807110802005"]}]},{"DOI":"10.13039\/501100017688","name":"Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100017688","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2025,7,15]]},"DOI":"10.1109\/jiot.2025.3557861","type":"journal-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T16:41:25Z","timestamp":1743784885000},"page":"26155-26168","source":"Crossref","is-referenced-by-count":3,"title":["Adaptive Model Compression for Efficient Federated Learning in IoT Systems"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3646-2349","authenticated-orcid":false,"given":"Xi","family":"Zhu","sequence":"first","affiliation":[{"name":"School of System Science and Engineering, Sun Yat-sen University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2748-8953","authenticated-orcid":false,"given":"Junbo","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7850-2121","authenticated-orcid":false,"given":"Kento","family":"Sato","sequence":"additional","affiliation":[{"name":"Data Management Platform Development Unit, RIKEN Center for Computational Science, Kobe, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7872-7718","authenticated-orcid":false,"given":"Zibin","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Sun Yat-sen University, Guangzhou, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2022.3140529"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2004.01.013"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/sp.2019.00065"},{"key":"ref4","article-title":"Federated optimization: Distributed optimization beyond the datacenter","author":"Kone\u010dn\u00fd","year":"2015","journal-title":"arXiv:1511.03575"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_1"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858232"},{"key":"ref7","first-page":"1","article-title":"QSGD: Communication-efficient SGD via gradient quantization and encoding","volume-title":"Proc. 31st Adv. Neural Inf. Process. Syst.","author":"Alistarh"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-29763-x"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3460120.3484579"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-89698-0_75"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2022.3186936"},{"key":"ref12","article-title":"Communication-efficient federated learning with adaptive compression under dynamic bandwidth","author":"Zhuansun","year":"2024","journal-title":"arXiv:2405.03248"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.298"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3066410"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2022.3154387"},{"key":"ref16","first-page":"3174","article-title":"Adaptive gradient quantization for data-parallel SGD","volume-title":"Proc. 34th Adv. Neural Inf. Process. Syst.","author":"Faghri"},{"key":"ref17","first-page":"1","article-title":"TernGrad: Ternary gradients to reduce communication in distributed deep learning","volume-title":"Proc. 31st Adv. Neural Inf. Process. Syst.","author":"Wen"},{"key":"ref18","article-title":"Federated optimization: Distributed machine learning for on-device intelligence","author":"Kone\u010dn\u00fd","year":"2016","journal-title":"arXiv:1610.02527"},{"key":"ref19","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Artif. Intell. Statist.","author":"McMahan"},{"key":"ref20","first-page":"14835","article-title":"Qimera: Data-free quantization with synthetic boundary supporting samples","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Choi"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488877"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904348"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3131614"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3166101"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9413697"},{"key":"ref26","first-page":"3973","article-title":"FedBoost: A communication-efficient algorithm for federated learning","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Hamer"},{"key":"ref27","first-page":"10","article-title":"LassoNet: Neural networks with feature sparsity","volume-title":"Proc. 24th Int. Conf. Artif. Intell. Statist.","author":"Lemhadri"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1214\/17-STS622"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2016.1260470"},{"key":"ref30","first-page":"525","article-title":"Follow-the-regularized-leader and mirror descent: Equivalence theorems and l1 regularization","volume-title":"Proc. 14th Int. Conf. Artif. Intell. Statist.","author":"McMahan"},{"issue":"57","key":"ref31","first-page":"1","article-title":"Normalizing flows for probabilistic modeling and inference","volume":"22","author":"Papamakarios","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM48880.2022.9796982"},{"key":"ref33","first-page":"1","article-title":"Sparsified SGD with memory","volume-title":"Proc. 32nd Adv. Neural Inf. Process. Syst.","author":"Stich"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICARCV.2018.8581332"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICIoT48696.2020.9089484"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.aej.2021.09.066"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6488907\/11074249\/10949156.pdf?arnumber=10949156","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T23:21:03Z","timestamp":1752103263000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10949156\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,15]]},"references-count":36,"journal-issue":{"issue":"14"},"URL":"https:\/\/doi.org\/10.1109\/jiot.2025.3557861","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"value":"2327-4662","type":"electronic"},{"value":"2372-2541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,15]]}}}