乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T06:37:51Z","timestamp":1725431871300},"reference-count":54,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"15","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"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":["52375128","62376215","62201458"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015401","name":"Key Research and Development Projects of Shaanxi Province","doi-asserted-by":"publisher","award":["2021ZDLGY06-05"],"id":[{"id":"10.13039\/501100015401","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Special Project of Shaanxi Provincial Department of Education","award":["23JK0664"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2024,8,1]]},"DOI":"10.1109\/jiot.2024.3396217","type":"journal-article","created":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T17:29:28Z","timestamp":1714670968000},"page":"26430-26443","source":"Crossref","is-referenced-by-count":1,"title":["Communication-Efficient and Privacy-Preserving Aggregation in Federated Learning With Adaptability"],"prefix":"10.1109","volume":"11","author":[{"given":"Xuehua","family":"Sun","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Xidian University, Xi’an, China"}]},{"ORCID":"http:\/\/orcid.org\/0009-0006-4955-0048","authenticated-orcid":false,"given":"Zengsen","family":"Yuan","sequence":"additional","affiliation":[{"name":"College of School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, China"}]},{"given":"Xianguang","family":"Kong","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Xidian University, Xi’an, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8069-3182","authenticated-orcid":false,"given":"Liang","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Guelph, Guelph, ON, Canada"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2515-8579","authenticated-orcid":false,"given":"Lang","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science, Xi’an University of Posts and Telecommunications, Xi’an, China"}]},{"given":"Ying","family":"Lin","sequence":"additional","affiliation":[{"name":"Shaanxi Hanlin Holdings Group co. ltd., Xi’an, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3002255"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2933890"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3281347"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_2"},{"key":"ref6","first-page":"1707","article-title":"QSGD: Communication-efficient SGD via gradient quantization and encoding","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Alistarh"},{"key":"ref7","article-title":"Stochastic distributed learning with gradient quantization and variance reduction","author":"Horv\u00e1th","year":"2019","journal-title":"arXiv:1904.05115"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11728"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.05.137"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/tmc.2023.3343288"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294915"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2014-274"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8852172"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2022.3185327"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2021.3128679"},{"key":"ref17","first-page":"7575","article-title":"cpSGD: Communication-efficient and differentially-private distributed SGD","volume-title":"Proc. 32nd Int. Conf. Neural Inf. Process. Syst.","author":"Agarwal"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.2988575"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3056991"},{"key":"ref20","article-title":"Learning differentially private recurrent languagemodels","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"McMahan"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17053"},{"key":"ref22","article-title":"Differentially private federated learning: A client level perspective","author":"Geyer","year":"2017","journal-title":"arXiv:1712.07557"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3174394"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2021.09.015"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1561\/9781601988195"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/217"},{"key":"ref27","first-page":"1","article-title":"Differentially private aggregation in the shuffle model: Almost central accuracy in almost a single message","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ghazi"},{"key":"ref28","first-page":"1","article-title":"Renyi differential privacy of the subsampled shuffle model in distributed learning","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Girgis"},{"key":"ref29","first-page":"17455","article-title":"Differentially private learning with adaptive clipping","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Andrew"},{"key":"ref30","article-title":"Three tools for practical differential privacy","author":"van der Veen","year":"2018","journal-title":"arXiv:1812.02890"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3258255"},{"key":"ref32","article-title":"AdaCliP: Adaptive clipping for private SGD","author":"Pichapati","year":"2019","journal-title":"arXiv:1908.07643"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3318944"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2956615"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3166101"},{"key":"ref36","first-page":"1","article-title":"Federated learning: Strategies for improving communication efficiency","volume-title":"Proc. Conf. Neural Inf. Process. Syst.","volume":"8","author":"Konecn\u1ef3"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.21437\/interspeech.2015-354"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d17-1045"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2991416"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2905659"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813677"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CSF.2017.11"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.29012\/jpc.723"},{"key":"ref45","first-page":"1","article-title":"Efficient privacypreserving stochastic nonconvex optimization","volume-title":"Proc. 29th Conf. Uncertain. Artif. Intell.","author":"Wang"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02352"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00988"},{"key":"ref48","article-title":"Deep gradient compression: Reducing the communication bandwidth for distributed training","author":"Lin","year":"2017","journal-title":"arXiv:1712.01887"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1966.1053907"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2211477"},{"key":"ref52","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017","journal-title":"arXiv:1708.07747"},{"key":"ref53","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2012"},{"key":"ref54","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv:1409.1556"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6488907\/10607832\/10517963.pdf?arnumber=10517963","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T18:41:33Z","timestamp":1721846493000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10517963\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,1]]},"references-count":54,"journal-issue":{"issue":"15"},"URL":"https:\/\/doi.org\/10.1109\/jiot.2024.3396217","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"value":"2327-4662","type":"electronic"},{"value":"2372-2541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,1]]}}}