{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T07:37:25Z","timestamp":1783409845114,"version":"3.54.6"},"reference-count":51,"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:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3554138","type":"journal-article","created":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T18:42:07Z","timestamp":1742841727000},"page":"54322-54337","source":"Crossref","is-referenced-by-count":4,"title":["Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning With Adaptive Quantization and Differential Privacy"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4324-8052","authenticated-orcid":false,"given":"Emre","family":"Ard\u0131\u00e7","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Gebze Technical University, Gebze, Kocaeli, T&#x00FC;rkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6952-6735","authenticated-orcid":false,"given":"Yakup","family":"Gen\u00e7","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gebze Technical University, Gebze, Kocaeli, T&#x00FC;rkiye"}],"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. Stat. (AISTATS)","volume":"54","author":"McMahan"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/2534169.2486006"},{"key":"ref4","article-title":"Protection against reconstruction and its applications in private federated learning","author":"Bhowmick","year":"2018","journal-title":"arXiv:1812.00984"},{"key":"ref5","first-page":"267","article-title":"The secret sharer: Evaluating and testing unintended memorization in neural networks","volume-title":"Proc. 28th USENIX Secur. Symp. (USENIX Secur.)","author":"Carlini"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-7970-4"},{"key":"ref8","article-title":"Federated learning: Strategies for improving communication efficiency","author":"Kone\u010dn\u00fd","year":"2016","journal-title":"arXiv:1610.05492"},{"key":"ref9","first-page":"1299","article-title":"Gradient sparsification for communication-efficient distributed optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"31","author":"Wangni"},{"key":"ref10","first-page":"3365","article-title":"ATOMO: Communication-efficient learning via atomic sparsification","volume-title":"Proc. 32nd Conf. Neural Inf. Process. Syst. (NeurIPS)","volume":"31","author":"Wang"},{"key":"ref11","article-title":"Expanding the reach of federated learning by reducing client resource requirements","author":"Caldas","year":"2018","journal-title":"arXiv:1812.07210"},{"key":"ref12","first-page":"7663","article-title":"Communication compression for decentralized training","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"31","author":"Tang"},{"key":"ref13","article-title":"Deep gradient compression: Reducing the communication bandwidth for distributed training","author":"Lin","year":"2017","journal-title":"arXiv:1712.01887"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2024.3363887"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2024.3382776"},{"key":"ref16","first-page":"559","article-title":"SignSGD: Compressed optimisation for non-convex problems","volume-title":"Proc. 35th Int. Conf. Mach. Learn. (ICML)","author":"Bernstein"},{"key":"ref17","first-page":"1508","article-title":"TernGrad: Ternary gradients to reduce communication in distributed deep learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Wen"},{"key":"ref18","first-page":"1","article-title":"QSGD: Communication-efficient SGD via gradient quantization and encoding","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Alistarh"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9413697"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM48099.2022.10001205"},{"key":"ref21","article-title":"FedAQ: Communication-efficient federated edge learning via joint uplink and downlink adaptive quantization","author":"Qu","year":"2024","journal-title":"arXiv:2406.18156"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00001"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2023.3325889"},{"key":"ref25","article-title":"Efficient private statistics with succinct sketches","author":"Melis","year":"2015","journal-title":"arXiv:1508.06110"},{"key":"ref26","article-title":"Differentially private federated learning: A client level perspective","author":"Geyer","year":"2017","journal-title":"arXiv:1712.07557"},{"key":"ref27","article-title":"Differentially private meta-learning","author":"Li","year":"2019","journal-title":"arXiv:1909.05830"},{"key":"ref28","article-title":"Learning differentially private recurrent language models","author":"McMahan","year":"2017","journal-title":"arXiv:1710.06963"},{"key":"ref29","first-page":"17455","article-title":"Differentially private learning with adaptive clipping","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Andrew"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"ref31","article-title":"Differentially private federated learning: A systematic review","author":"Fu","year":"2024","journal-title":"arXiv:2405.08299"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/OJCOMS.2024.3438264"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2023.3244092"},{"key":"ref34","article-title":"A novel privacy enhancement scheme with dynamic quantization for federated learning","author":"Wang","year":"2024","journal-title":"arXiv:2405.16058"},{"key":"ref35","article-title":"Randomized quantization is all you need for differential privacy in federated learning","author":"Youn","year":"2023","journal-title":"arXiv:2306.11913"},{"key":"ref36","article-title":"Optimal privacy preserving for federated learning in mobile edge computing","author":"Nguyen","year":"2022","journal-title":"arXiv:2211.07166"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2211477"},{"key":"ref38","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref39","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","volume":"2","author":"Li"},{"key":"ref40","article-title":"Integer quantization for deep learning inference: Principles and empirical evaluation","author":"Wu","year":"2020","journal-title":"arXiv:2004.09602"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.2988575"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.001.1900506"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737416"},{"key":"ref44","article-title":"FedML: A research library and benchmark for federated machine learning","author":"He","year":"2020","journal-title":"arXiv:2007.13518"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_11"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488723"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/SIU59756.2023.10223784"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.jestch.2024.101920"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2018.8451588"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.17632\/rscbjbr9sj.3"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2015.2496264"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/10937694.pdf?arnumber=10937694","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T00:24:59Z","timestamp":1743639899000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10937694\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":51,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3554138","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}