{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T20:04:34Z","timestamp":1785182674277,"version":"3.55.0"},"reference-count":43,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":"crossref","award":["62376048"],"award-info":[{"award-number":["62376048"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/tifs.2026.3711861","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T19:42:27Z","timestamp":1783626147000},"page":"6420-6435","source":"Crossref","is-referenced-by-count":0,"title":["Empowering Non-IID Federated Learning With Data Augmentation and Data-Free Knowledge Distillation"],"prefix":"10.1109","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0273-2665","authenticated-orcid":false,"given":"Furui","family":"Zhan","sequence":"first","affiliation":[{"name":"Dalian Maritime University","place":["Dalian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3448-7432","authenticated-orcid":false,"given":"Ziyu","family":"Deng","sequence":"additional","affiliation":[{"name":"Dalian Maritime University","place":["Dalian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7278-1635","authenticated-orcid":false,"given":"Yingxin","family":"Liu","sequence":"additional","affiliation":[{"name":"Dalian Maritime University","place":["Dalian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9157-6050","authenticated-orcid":false,"given":"Chengwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Dalian Maritime University","place":["Dalian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralizeddata","author":"McMahan","year":"2017","journal-title":"Proc. Int. Conf. Artif. Intell. Stat. (AISTATS)"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3136853"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2020.3045266"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3510033"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.16960"},{"key":"ref7","article-title":"The non-IID data quagmire of decentralized machine learning","author":"Hsieh","year":"2019","journal-title":"arXiv:1910.00189"},{"key":"ref8","article-title":"On the convergence of local descent methods in federated learning","author":"Haddadpour","year":"2019","journal-title":"arXiv:1910.14425"},{"key":"ref9","article-title":"Adaptive federated optimization","author":"Reddi","year":"2020","journal-title":"arXiv:2003.00295"},{"key":"ref10","article-title":"From local SGD to local fixed-point methods for federated learning","author":"Malinovsky","year":"2020","journal-title":"arXiv:2004.01442"},{"key":"ref11","article-title":"Federated learning with non-IID data","author":"Zhao","year":"2018","journal-title":"arXiv:1806.00582"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/icde53745.2022.00077"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI50451.2021.9660072"},{"key":"ref14","article-title":"Measuring the effects of non-identical data distribution for federated visual classification","author":"Hsu","year":"2019","journal-title":"arXiv:1909.06335"},{"key":"ref15","first-page":"4519","article-title":"Tighter theory for local SGD on identical and heterogeneous data","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Khaled"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.52202\/068431-2787"},{"key":"ref17","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. 3rd Mach. Learn. Syst. Conf.","author":"Li"},{"key":"ref18","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","author":"Karimireddy","year":"2019","journal-title":"arXiv:1910.06378"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2023.3323645"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01567"},{"key":"ref21","article-title":"Federated learning based on dynamic regularization","author":"Acar","year":"2021","journal-title":"arXiv:2111.04263"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2021.3115952"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE55515.2023.00164"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3421602"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.111764"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICCD46524.2019.00038"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00780"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2022.3177748"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3309858"},{"key":"ref30","article-title":"Training federated GANs with theoretical guarantees: A universal aggregation approach","author":"Zhang","year":"2021","journal-title":"arXiv:2102.04655"},{"key":"ref31","article-title":"Ensemble distillation for robust model fusion in federated learning","author":"Lin","year":"2020","journal-title":"arXiv:2006.07242"},{"key":"ref32","article-title":"FedBE: Making Bayesian model ensemble applicable to federated learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Chen"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00361"},{"key":"ref34","article-title":"Data-free adversarial distillation","author":"Fang","year":"2019","journal-title":"arXiv:1912.11006"},{"key":"ref35","article-title":"Conditional generative adversarial nets","author":"Mirza","year":"2014","journal-title":"arXiv:1411.1784"},{"key":"ref36","first-page":"2642","article-title":"Conditional image synthesis with auxiliary classifier GANs","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Odena"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413814"},{"key":"ref38","article-title":"Model fusion via optimal transport","author":"Singh","year":"2019","journal-title":"arXiv:1910.05653"},{"key":"ref39","article-title":"FedBN: Federated learning on non-IID features via local batch normalization","author":"Li","year":"2021","journal-title":"arXiv:2102.07623"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.52202\/079017-3757"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01955"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3126742"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10206\/11313711\/11602114.pdf?arnumber=11602114","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T19:13:29Z","timestamp":1785179609000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11602114\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":43,"URL":"https:\/\/doi.org\/10.1109\/tifs.2026.3711861","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}