{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T02:47:12Z","timestamp":1778899632912,"version":"3.51.4"},"reference-count":80,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&D Program of China","award":["2022YFB3102100"],"award-info":[{"award-number":["2022YFB3102100"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62302343"],"award-info":[{"award-number":["62302343"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172303"],"award-info":[{"award-number":["62172303"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key R&D Program of Hubei Province","award":["2024BAB018"],"award-info":[{"award-number":["2024BAB018"]}]},{"name":"Key R&D Program of Shandong Province","award":["2022CXPT055"],"award-info":[{"award-number":["2022CXPT055"]}]},{"name":"Wuhan City Joint Innovation Laboratory for Next-Generation Wireless Communication Industry Featuring Satellite-Terrestrial Integration","award":["4050902040448"],"award-info":[{"award-number":["4050902040448"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Dependable and Secure Comput."],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1109\/tdsc.2025.3597635","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T17:46:04Z","timestamp":1754934364000},"page":"7463-7478","source":"Crossref","is-referenced-by-count":1,"title":["GetFed: Accurate, Differentially Private Federated Learning With GAN-Based Data Generation"],"prefix":"10.1109","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9719-9313","authenticated-orcid":false,"given":"Hao","family":"Bai","sequence":"first","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3472-419X","authenticated-orcid":false,"given":"Kun","family":"He","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0816-5777","authenticated-orcid":false,"given":"Yuqing","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7212-5297","authenticated-orcid":false,"given":"Jing","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haowei","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongmou","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0883-6278","authenticated-orcid":false,"given":"Xuanang","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3634-3385","authenticated-orcid":false,"given":"Ruiying","family":"Du","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Artif. Intell. Statist.","author":"McMahan","year":"2017"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS47774.2020.00171"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3077803"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2022.23153"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-021-01506-3"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i11.21505"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.001.2100102"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2022.06.010"},{"key":"ref9","article-title":"Federated learning with non-IID data","author":"Zhao","year":"2018"},{"key":"ref10","first-page":"4387","article-title":"The non-IID data quagmire of decentralized machine learning","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Hsieh","year":"2020"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1561\/9781680837896"},{"key":"ref12","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","author":"Li","year":"2020"},{"key":"ref13","first-page":"143","article-title":"Differentially private federated learning on heterogeneous data","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Noble","year":"2022"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58607-2_5"},{"key":"ref15","article-title":"Federated learning based on dynamic regularization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Acar","year":"2021"},{"key":"ref16","first-page":"1595","article-title":"$\\lbrace${PrivateFL$\\rbrace$}: Accurate, differentially private federated learning via personalized data transformation","volume-title":"Proc. 32nd USENIX Secur. Symp.","author":"Yang","year":"2023"},{"key":"ref17","first-page":"7611","article-title":"Tackling the objective inconsistency problem in heterogeneous federated optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wang","year":"2020"},{"key":"ref18","article-title":"Fedbn: Federated learning on non-IID features via local batch normalization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li","year":"2021"},{"key":"ref19","article-title":"Measuring the effects of non-identical data distribution for federated visual classification","volume-title":"Proc. NeurIPS Workshop Federated Learn.","author":"Hsu","year":"2019"},{"key":"ref20","first-page":"14068","article-title":"Group knowledge transfer: Federated learning of large CNNs at the edge","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"He","year":"2020"},{"key":"ref21","first-page":"12878","article-title":"Data-free knowledge distillation for heterogeneous federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhu","year":"2021"},{"key":"ref22","first-page":"10533","article-title":"Dres-FL: Dropout-resilient secure federated learning for non-IID clients via secret data sharing","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Shao","year":"2022"},{"key":"ref23","article-title":"Communication-efficient on-device machine learning: Federated distillation and augmentation under non-IID private data","volume-title":"Proc. NIPS Workshop Mach. Learn. Phone Other Consum. Devices (MLPCD 2)","author":"Jeong","year":"2018"},{"key":"ref24","article-title":"Federated learning via synthetic data","author":"Goetz","year":"2020"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-28996-5_2"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3167482"},{"key":"ref28","article-title":"PFL-GAN: When client heterogeneity meets generative models in personalized federated learning","author":"Wijesinghe","year":"2023"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2025.3555193"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00029"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00065"},{"key":"ref32","first-page":"16937","article-title":"Inverting gradients-How easy is it to break privacy in federated learning?","volume-title":"Proc. 34th Int. Conf. Neural Inf. Process. Syst.","author":"Geiping","year":"2020"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/11787006_1"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-020-00780-4"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3339750"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.2988575"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3293417"},{"key":"ref38","article-title":"SoK: Gradient leakage in federated learning","author":"Du","year":"2024"},{"key":"ref39","first-page":"7559","article-title":"Differentiable augmentation for data-efficient GAN training","volume-title":"Proc. 34th Int. Conf. Neural Inf. Process. Syst.","author":"Zhao","year":"2020"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.12.003"},{"key":"ref41","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Radford","year":"2016"},{"key":"ref42","first-page":"214","article-title":"Wasserstein generative adversarial networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Arjovsky","year":"2017"},{"key":"ref43","first-page":"2180","article-title":"InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Chen","year":"2016"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM51629.2021.00084"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.2478\/popets-2019-0008"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2019.8900084"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1561\/9781601988195"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM48880.2022.9796841"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM53939.2023.10228873"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00989"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00531"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2023.3310094"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1016\/j.jisa.2023.103475"},{"key":"ref54","first-page":"1291","article-title":"{Updates-Leak} : Data set inference and reconstruction attacks in online learning","volume-title":"Proc. 29th USENIX Secur. Symp.","author":"Salem","year":"2020"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00018"},{"issue":"11","key":"ref56","article-title":"Frechet inception distance (FID) for evaluating gans","volume":"3","author":"Yu","year":"2021"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01138"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/tbdata.2022.3190835"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref61","article-title":"Fashion-mnist: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017"},{"key":"ref62","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.2118\/18761-MS"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.4324\/9781410605337-29"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref67","article-title":"Federated learning with matched averaging","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Wang","year":"2020"},{"key":"ref68","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Karimireddy","year":"2020"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3510756"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOM56429.2023.10099110"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00369"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1145\/3642968.3654813"},{"key":"ref73","first-page":"6357","article-title":"Ditto: Fair and robust federated learning through personalization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li","year":"2021"},{"key":"ref74","article-title":"Fedmix: Approximation of mixup under mean augmented federated learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Yoon","year":"2021"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2023.3278668"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.12"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"ref78","first-page":"493","article-title":"$\\lbrace${BatchCrypt$\\rbrace$}: Efficient homomorphic encryption for $\\lbrace${Cross-Silo$\\rbrace$} federated learning","volume-title":"Proc. 2020 USENIX Annu. Techn. Conf.","author":"Zhang","year":"2020"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2017.2787987"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.2751"}],"container-title":["IEEE Transactions on Dependable and Secure Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/8858\/11242243\/11122422.pdf?arnumber=11122422","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T21:01:01Z","timestamp":1763154061000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11122422\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":80,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tdsc.2025.3597635","relation":{},"ISSN":["1545-5971","1941-0018","2160-9209"],"issn-type":[{"value":"1545-5971","type":"print"},{"value":"1941-0018","type":"electronic"},{"value":"2160-9209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11]]}}}