{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T06:27:04Z","timestamp":1762324024041,"version":"build-2065373602"},"reference-count":42,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":["62202157"],"award-info":[{"award-number":["62202157"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001321","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"publisher","award":["2025A1515010117"],"award-info":[{"award-number":["2025A1515010117"]}],"id":[{"id":"10.13039\/501100001321","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tifs.2025.3616594","type":"journal-article","created":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T17:39:25Z","timestamp":1760463565000},"page":"11668-11683","source":"Crossref","is-referenced-by-count":0,"title":["Enabling Gradient Inversion Attack Against SplitFed Learning via\n                    <i>L<\/i>\n                    <sub>2<\/sub>\n                    Norm Amplification"],"prefix":"10.1109","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1859-3436","authenticated-orcid":false,"given":"Jianan","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Cyber Science and Technology, Hunan University, Changsha, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9607-8974","authenticated-orcid":false,"given":"Wenjuan","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Cyber Science and Technology, Hunan University, Changsha, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4262-153X","authenticated-orcid":false,"given":"Kuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Nebraska&#x2013;Lincoln, Lincoln, NE, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7372-2539","authenticated-orcid":false,"given":"Hongbo","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Electronic Engineering, Hunan University, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20825"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2018.05.003"},{"key":"ref3","article-title":"Split learning for collaborative deep learning in healthcare","author":"Poirot","year":"2019","journal-title":"arXiv:1912.12115"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2023.3242704"},{"key":"ref5","first-page":"374","article-title":"Towards federated learning at scale: System design","volume-title":"Proc. Mach. Learn. Syst.","author":"Bonawitz"},{"key":"ref6","article-title":"Federated optimization: Distributed machine learning for on-device intelligence","author":"Konec\u02c7n\u00fd","year":"2016","journal-title":"arXiv:1610.02527"},{"key":"ref7","article-title":"Federated learning: Strategies for improving communication efficiency","author":"Konec\u02c7n\u00fd","year":"2016","journal-title":"arXiv:1610.05492"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM53939.2023.10228954"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2023.3331690"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2023.3327372"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2023.3322755"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2024.3401096"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2024.3411791"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2022.3213411"},{"key":"ref15","article-title":"Split federated learning for 6G enabled-networks: Requirements, challenges and future directions","author":"Hafi","year":"2023","journal-title":"arXiv:2309.09086"},{"key":"ref16","first-page":"24617","article-title":"Federated split vision transformer for COVID-19 CXR diagnosis using task-agnostic training","volume-title":"Proc. Conf. Neural Inf. Process. Syst.","author":"Park"},{"key":"ref17","first-page":"14774","article-title":"Deep leakage from gradients","volume-title":"Proc. Int. Conf. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Zhu"},{"key":"ref18","first-page":"16937","article-title":"Inverting gradients\u2014How easy is it to break privacy in federated learning?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Geiping"},{"key":"ref19","first-page":"29898","article-title":"Gradient inversion with generative image prior","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Jeon"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00989"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01607"},{"key":"ref22","first-page":"23668","article-title":"Fishing for user data in large-batch federated learning via gradient magnification","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Wen"},{"article-title":"Robbing the fed: Directly obtaining private data in federated learning with modified models","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Fowl","key":"ref23"},{"key":"ref24","first-page":"5959","article-title":"Gradient disaggregation: Breaking privacy in federated learning by reconstructing the user participant matrix","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Lam"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3548606.3560557"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/0167-2789(92)90242-F"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00813"},{"article-title":"Large scale GAN training for high fidelity natural image synthesis","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Brock","key":"ref30"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00919"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS51616.2021.00081"},{"key":"ref33","article-title":"Variance-based gradient compression for efficient distributed deep learning","author":"Tsuzuku","year":"2018","journal-title":"arXiv:1802.06058"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"Krizhevsky","key":"ref35"},{"article-title":"Tiny imagenet visual recognition challenge","year":"2015","author":"Le","key":"ref36"},{"article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Simonyan","key":"ref37"},{"key":"ref38","first-page":"5769","article-title":"Improved training of Wasserstein GANs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Gulrajani"},{"key":"ref39","article-title":"The CMA evolution strategy: A tutorial","author":"Hansen","year":"2016","journal-title":"arXiv:1604.00772"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i8.26163"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00995"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10206\/10810755\/11201035.pdf?arnumber=11201035","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T05:55:12Z","timestamp":1762322112000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11201035\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":42,"URL":"https:\/\/doi.org\/10.1109\/tifs.2025.3616594","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"type":"print","value":"1556-6013"},{"type":"electronic","value":"1556-6021"}],"subject":[],"published":{"date-parts":[[2025]]}}}