{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T20:07:06Z","timestamp":1780949226468,"version":"3.54.1"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"7","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DE200100863"],"award-info":[{"award-number":["DE200100863"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Joint Technology and Innovation Research Centre"},{"name":"University of Technology Sydney and Vietnam National University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Mobile Comput."],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1109\/tmc.2026.3665886","type":"journal-article","created":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T21:10:46Z","timestamp":1771362646000},"page":"10335-10350","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive Quantization and Differential Privacy Federated Learning Framework"],"prefix":"10.1109","volume":"25","author":[{"given":"Minh Hai","family":"Nguyen","sequence":"first","affiliation":[{"name":"School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6903-0163","authenticated-orcid":false,"given":"Chi-Hieu","family":"Nguyen","sequence":"additional","affiliation":[{"name":"School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2659-8648","authenticated-orcid":false,"given":"Diep N.","family":"Nguyen","sequence":"additional","affiliation":[{"name":"School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9528-0863","authenticated-orcid":false,"given":"Dinh Thai","family":"Hoang","sequence":"additional","affiliation":[{"name":"School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7269-2286","authenticated-orcid":false,"given":"Mohammad Abu","family":"Alsheikh","sequence":"additional","affiliation":[{"name":"Faculty of Science &#x0026; Technology, University of Canberra, Bruce, ACT, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Communication efficiency in federated learning: Achievements and challenges","author":"Shahid","year":"2021"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM53939.2023.10228953"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3124599"},{"key":"ref4","article-title":"iDLG: Improved deep leakage from gradients","author":"Zhao","year":"2020"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_2"},{"key":"ref6","article-title":"Recurrent neural network regularization","author":"Zaremba","year":"2014"},{"key":"ref7","first-page":"7564","article-title":"CPSGD: Communication-efficient and differentially-private distributed SGD","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Agarwal","year":"2018"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1561\/9781601988195"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3166101"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2022.3190510"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2023.3348163"},{"key":"ref12","article-title":"CosSGD: Communication-efficient federated learning with a simple cosine-based quantization","author":"He","year":"2020"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2023.3244092"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/MeditCom61057.2024.10621075"},{"key":"ref15","first-page":"1","article-title":"Randomized quantization is all you need for differential privacy in federated learning","volume-title":"Proc. Federated Learn. Anal. Pract., Algorithms, Syst., Appl., Opportunities","author":"Youn","year":"2023"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9413697"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM53939.2023.10228970"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3510587"},{"key":"ref19","first-page":"8852","article-title":"DAdaQuant: Doubly-adaptive quantization for communication-efficient federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"H\u00f6nig","year":"2022"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM52923.2024.10901124"},{"key":"ref21","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Artif. Intell. Statist.","volume":"54","author":"Mcmahan","year":"2017"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2008.27"},{"key":"ref23","first-page":"2021","article-title":"FedPAQ: A communication-efficient federated learning method with periodic averaging and quantization","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Reisizadeh","year":"2020"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9054168"},{"key":"ref25","first-page":"2350","article-title":"Federated learning with compression: Unified analysis and sharp guarantees","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Haddadpour","year":"2021"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2022.3151126"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfranklin.2022.12.053"},{"key":"ref28","first-page":"10110","article-title":"Differentially private federated learning on heterogeneous data","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Noble","year":"2022"},{"key":"ref29","article-title":"Federated learning with differential privacy","author":"Banse","year":"2024"},{"key":"ref30","article-title":"MicroFedML: Privacy preserving federated learning for small weights","volume":"2022","author":"Guo","year":"2022","journal-title":"IACR Cryptology ePrint Arch."},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.56553\/popets-2023-0009"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2024.3365815"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3382875"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/icc45041.2023.10279544"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1561\/0400000042"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1137\/120880811"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCA62789.2024.10591815"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICARCV63323.2024.10821649"},{"key":"ref39","first-page":"394","article-title":"Improving the Gaussian mechanism for differential privacy: Analytical calibration and optimal denoising","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Balle","year":"2018"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2010.12"},{"key":"ref41","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Artif. Intell. Statist.","author":"McMahan","year":"2017"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3037554"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/OJCOMS.2024.3425531"}],"container-title":["IEEE Transactions on Mobile Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/7755\/11552380\/11397834.pdf?arnumber=11397834","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T19:54:56Z","timestamp":1780948496000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11397834\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":45,"journal-issue":{"issue":"7"},"URL":"https:\/\/doi.org\/10.1109\/tmc.2026.3665886","relation":{},"ISSN":["1536-1233","1558-0660","2161-9875"],"issn-type":[{"value":"1536-1233","type":"print"},{"value":"1558-0660","type":"electronic"},{"value":"2161-9875","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7]]}}}