{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T17:51:26Z","timestamp":1781977886940,"version":"3.54.5"},"reference-count":69,"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"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tifs.2024.3516564","type":"journal-article","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T19:09:06Z","timestamp":1734030546000},"page":"822-838","source":"Crossref","is-referenced-by-count":10,"title":["Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization"],"prefix":"10.1109","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4089-3727","authenticated-orcid":false,"given":"Wenrui","family":"Yu","sequence":"first","affiliation":[{"name":"CISPA Helmholtz Center for Information Security, Saarbr&#x00FC;cken, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2487-5149","authenticated-orcid":false,"given":"Qiongxiu","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electronic Systems, Aalborg University, Aalborg, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5687-854X","authenticated-orcid":false,"given":"Milan","family":"Lopuha\u00e4-Zwakenberg","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3586-7969","authenticated-orcid":false,"given":"Mads","family":"Gr\u00e6sb\u00f8ll Christensen","sequence":"additional","affiliation":[{"name":"Department of Electronic Systems, Aalborg University, Aalborg, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5998-1550","authenticated-orcid":false,"given":"Richard","family":"Heusdens","sequence":"additional","affiliation":[{"name":"Netherlands Defence Academy, Den Helder, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"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"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref3","article-title":"How to scale distributed deep learning?","author":"Jin","year":"2016","journal-title":"arXiv:1611.04581"},{"key":"ref4","first-page":"5330","article-title":"Can decentralized algorithms outperform centralized algorithms? A case study for decentralized parallel stochastic gradient descent","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Lian"},{"key":"ref5","first-page":"4848","article-title":"D2: Decentralized training over decentralized data","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tang"},{"key":"ref6","article-title":"Decentralized federated learning: A segmented gossip approach","author":"Hu","year":"2019","journal-title":"arXiv:1908.07782"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2013.2254478"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2019.2957719"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3058116"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2017.2672403"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2018.2876754"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403109"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_2"},{"key":"ref14","article-title":"IDLG: Improved deep leakage from gradients","author":"Zhao","year":"2020","journal-title":"arXiv:2001.02610"},{"key":"ref15","first-page":"16937","article-title":"Inverting gradients-how easy is it to break privacy in federated learning?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Bauermeister"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01607"},{"key":"ref17","article-title":"When the curious abandon honesty: Federated learning is not private","author":"Boenisch","year":"2021","journal-title":"arXiv:2112.02918"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2023.3239116"},{"key":"ref19","article-title":"A framework for evaluating gradient leakage attacks in federated learning","author":"Wei","year":"2020","journal-title":"arXiv:2004.10397"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3227761"},{"key":"ref21","article-title":"Deep leakage from model in federated learning","author":"Zhao","year":"2022","journal-title":"arXiv:2206.04887"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/SRDS55811.2022.00012"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1137\/130943170"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-23502-4_10"},{"key":"ref25","first-page":"28004","article-title":"RelaySum for decentralized deep learning on heterogeneous data","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Vogels"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/SP46215.2023.10179291"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2010.2052531"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1137\/060678324"},{"key":"ref29","first-page":"5381","article-title":"A unified theory of decentralized SGD with changing topology and local updates","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"1","author":"Koloskova"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1561\/2200000016"},{"issue":"1","key":"ref31","first-page":"3","article-title":"Primer on monotone operator methods","volume":"15","author":"Ryu","year":"2016","journal-title":"Appl. Comput. Math."},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/138027.138036"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/11787006_1"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/11681878_14"},{"key":"ref35","volume-title":"Elements of Information Theory","author":"Cover","year":"2012"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3050064"},{"key":"ref37","article-title":"Information-theoretic metrics for local differential privacy protocols","author":"Lopuha\u00e4-Zwakenberg","year":"2019","journal-title":"arXiv:1910.07826"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.23919\/Eusipco47968.2020.9287429"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/SPAWC48557.2020.9154277"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2020.2991139"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3343599"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9413539"},{"key":"ref43","article-title":"Layer-wise characterization of latent information leakage in federated learning","author":"Mo","year":"2020","journal-title":"arXiv:2010.08762"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978308"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2011.26"},{"key":"ref46","first-page":"33","article-title":"Differential privacy under fire","volume-title":"Proc. 20th USENIX Conf. Secur.","author":"Haeberlen"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/IHMSC49165.2020.00057"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00029"},{"key":"ref49","article-title":"User-level label leakage from gradients in federated learning","author":"Wainakh","year":"2021","journal-title":"arXiv:2105.09369"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/3359789.3359824"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737416"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/3319535.3354261"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00033"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref56","first-page":"1","article-title":"Effective passive membership inference attacks in federated learning against overparameterized models","volume-title":"Proc. 11th Int. Conf. Learn. Represent.","author":"Li"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CSF.2018.00027"},{"key":"ref58","first-page":"2615","article-title":"Systematic evaluation of privacy risks of machine learning models","volume-title":"Proc. USENIX Secur. Symp.","author":"Song"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2022.108456"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.3029887"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8461782"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053348"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.66.016121"},{"key":"ref64","first-page":"29898","article-title":"Gradient inversion with generative image prior","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Jeon"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2211477"},{"key":"ref67","article-title":"Learning multiple layers of features from tiny images","author":"Alex","year":"2009"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2018.2806844"},{"key":"ref69","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv:1409.1556"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10206\/10810755\/10795238.pdf?arnumber=10795238","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T19:42:00Z","timestamp":1736797320000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10795238\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":69,"URL":"https:\/\/doi.org\/10.1109\/tifs.2024.3516564","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}