{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:40:25Z","timestamp":1778604025576,"version":"3.51.4"},"reference-count":217,"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\/4.0\/legalcode"}],"funder":[{"name":"Ministry of Higher Education of Malaysia\u2019s Fundamental Research Grant Scheme","award":["FRGS\/1\/2023\/ICT07\/MMU\/01\/1"],"award-info":[{"award-number":["FRGS\/1\/2023\/ICT07\/MMU\/01\/1"]}]},{"name":"Multimedia University (MMU) Research Scholarship"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3593953","type":"journal-article","created":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T18:52:04Z","timestamp":1753901524000},"page":"135138-135164","source":"Crossref","is-referenced-by-count":4,"title":["A Comprehensive Review of Cryptographic Techniques in Federated Learning for Secure Data Sharing and Applications"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7941-3260","authenticated-orcid":false,"given":"Anik","family":"Sen","sequence":"first","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3627-2131","authenticated-orcid":false,"given":"Swee-Huay","family":"Heng","sequence":"additional","affiliation":[{"name":"Centre for Intelligent Cloud Computing, CoE for Advanced Cloud, Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1267-1894","authenticated-orcid":false,"given":"Shing-Chiang","family":"Tan","sequence":"additional","affiliation":[{"name":"Centre for Advanced Analytics, CoE for Artificial Intelligence, Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/791"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106854"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.csi.2021.103583"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102004"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110677"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s11235-022-00982-3"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/GCWkshps52748.2021.9682053"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.08.062"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3528580.3532845"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/I3CS58314.2023.10127502"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3114581"},{"key":"ref13","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":"ref14","article-title":"Applied federated learning: Improving Google keyboard query suggestions","author":"Yang","year":"2018","journal-title":"arXiv:1812.02903"},{"key":"ref15","first-page":"1","article-title":"Attack detection using federated learning in medical cyber-physical systems","volume-title":"Proc. 28th Int. Conf. Comput. Commun. Netw. (ICCCN)","volume":"29","author":"Schneble"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref17","article-title":"Three approaches for personalization with applications to federated learning","author":"Mansour","year":"2020","journal-title":"arXiv:2002.10619"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3390\/e25081205"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-022-01046-x"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/tit.2022.3192506"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.3390\/electronics14020361"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/IC3SE62002.2024.10593458"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1155\/2024\/8138644"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.aej.2024.12.070"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/OJCS.2025.3536562"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3658644.3690191"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.10.007"},{"key":"ref28","first-page":"493","article-title":"BatchCrypt: Efficient homomorphic encryption for cross-silo federated learning","volume-title":"Proc. USENIX Annu. Tech. Conf. (USENIX ATC)","author":"Zhang"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM52122.2024.10621440"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512233"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM38437.2019.9013160"},{"key":"ref32","first-page":"1","article-title":"ESGD: Communication efficient distributed deep learning on the edge","volume-title":"Proc. USENIX Workshop Hot Topics Edge Comput. (HotEdge)","author":"Tao"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9149138"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/LNET.2019.2947144"},{"key":"ref35","article-title":"Can you really backdoor federated learning?","author":"Sun","year":"2019","journal-title":"arXiv:1911.07963"},{"key":"ref36","first-page":"2938","article-title":"How to backdoor federated learning","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Bagdasaryan"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1186\/s42400-024-00232-w"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/3128572.3140451"},{"key":"ref39","article-title":"Poisoning attacks against support vector machines","author":"Biggio","year":"2012","journal-title":"arXiv:1206.6389"},{"key":"ref40","first-page":"634","article-title":"Analyzing federated learning through an adversarial lens","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Bhagoji"},{"key":"ref41","first-page":"1605","article-title":"Local model poisoning attacks to Byzantine-robust federated learning","volume-title":"Proc. 29th USENIX Secur. Symp. (USENIX Secur.)","author":"Fang"},{"key":"ref42","first-page":"1","article-title":"Poison frogs! Targeted clean-label poisoning attacks on neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Shafahi"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00065"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00470-5_13"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3166101"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107235"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3124020"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.mfglet.2020.04.011"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3134755"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/3387107"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3107783"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488743"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3102155"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107700"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2022.3151126"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3036166"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2945367"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/MDM58254.2023.00042"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3302065"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3090951"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3128155"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/VTC2023-Spring57618.2023.10199280"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3085960"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/ISPDS58840.2023.10235627"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/CCGrid49817.2020.00-52"},{"key":"ref66","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Karimireddy"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9413764"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3075683"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3165945"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2024.3350232"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3022911"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3081560"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148862"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3108434"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2020.2988525"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2942190"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/ICAIC60265.2024.10433836"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107854"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-99-7032-2_1"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2023.3308331"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2024.03.016"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.3390\/app12020734"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2022.3153135"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.12.003"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2977383"},{"key":"ref86","article-title":"Adaptive federated optimization","author":"Reddi","year":"2020","journal-title":"arXiv:2003.00295"},{"key":"ref87","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"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11071072"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3189361"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/COMPSAC48688.2020.00-96"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2021.11.028"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2021.3054610"},{"key":"ref93","first-page":"694","article-title":"LightSecAgg: A lightweight and versatile design for secure aggregation in federated learning","volume-title":"Proc. Mach. Learn. Syst.","author":"So"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3118354"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2022.022290"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1145\/3338501.3357370"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3264259"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.3390\/s23041966"},{"key":"ref99","article-title":"Federated learning with matched averaging","author":"Wang","year":"2020","journal-title":"arXiv:2002.06440"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3311967"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.2988575"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3056991"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3110784"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2020.2994391"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3073925"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2020.3005909"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-021-03399-w"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3138693"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1109\/CSCWD57460.2023.10152829"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2021.3074185"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/ISCC58397.2023.10218066"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106775"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3111088"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148628"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3140806"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3052183"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3017377"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2987958"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.3390\/cryptography7040048"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.58346\/JISIS.2024.I3.021"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2874978"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3388992"},{"key":"ref123","first-page":"10912","article-title":"Secure quantized training for deep learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Keller"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.05.005"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102754"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2023.3285070"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.12.102"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.3390\/e24111545"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00078"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1109\/TrustCom50675.2020.00098"},{"key":"ref131","first-page":"4961","article-title":"CrypTen: Secure multi-party computation meets machine learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Knott"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1109\/SP40001.2021.00098"},{"key":"ref133","first-page":"6326","article-title":"Privacy-preserving feature selection with secure multiparty computation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1145\/3465377"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.1007\/s41635-017-0025-y"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.02.037"},{"key":"ref137","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8683546"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1109\/BDCloud.2018.00164"},{"key":"ref139","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2019.1800286"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.07.069"},{"key":"ref141","doi-asserted-by":"publisher","DOI":"10.1109\/SPW.2019.00041"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.1109\/IoTDI49375.2020.00017"},{"key":"ref143","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.2976321"},{"key":"ref144","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2018.8647649"},{"key":"ref145","first-page":"52","article-title":"Visual inspection with federated learning","volume-title":"Proc. Int. Conf. Image Anal. Recognit.","author":"Xu"},{"key":"ref146","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2962873"},{"key":"ref147","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM38437.2019.9013587"},{"key":"ref148","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-23551-2_2"},{"key":"ref149","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6096"},{"key":"ref150","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098118"},{"key":"ref151","article-title":"Federated and differentially private learning for electronic health records","author":"Pfohl","year":"2019","journal-title":"arXiv:1911.05861"},{"key":"ref152","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0230706"},{"key":"ref153","doi-asserted-by":"publisher","DOI":"10.2196\/medinform.7744"},{"key":"ref154","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759317"},{"key":"ref155","article-title":"HHHFL: Hierarchical heterogeneous horizontal federated learning for electroencephalography","author":"Gao","year":"2019","journal-title":"arXiv:1909.05784"},{"key":"ref156","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W19-5030"},{"key":"ref157","doi-asserted-by":"publisher","DOI":"10.1145\/3214303"},{"key":"ref158","doi-asserted-by":"publisher","DOI":"10.1145\/3381006"},{"key":"ref159","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2023.3268186"},{"key":"ref160","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/4376418"},{"key":"ref161","doi-asserted-by":"publisher","DOI":"10.23919\/ICACT53585.2022.9728772"},{"key":"ref162","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3037474"},{"key":"ref163","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2023.3235950"},{"key":"ref164","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-024-04957-8"},{"key":"ref165","doi-asserted-by":"publisher","DOI":"10.1016\/j.jisa.2021.102748"},{"key":"ref166","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2021.3076767"},{"key":"ref167","article-title":"Decentralized healthcare systems with federated learning and blockchain","author":"Zekiye","year":"2023","journal-title":"arXiv:2306.17188"},{"key":"ref168","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106019"},{"key":"ref169","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2023.02.021"},{"issue":"1","key":"ref170","first-page":"1","article-title":"Research and practice of financial credit risk management based on federated learning","volume":"31","author":"Li","year":"2023","journal-title":"Eng. Lett."},{"key":"ref171","article-title":"Federated learning used to detect credit card fraud","author":"Jansson","year":"2020"},{"key":"ref172","doi-asserted-by":"publisher","DOI":"10.1109\/RDAAPS48126.2021.9452005"},{"key":"ref173","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-70604-3_5"},{"key":"ref174","doi-asserted-by":"publisher","DOI":"10.3390\/s21010167"},{"key":"ref175","doi-asserted-by":"publisher","DOI":"10.46569\/9s161f47k"},{"key":"ref176","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-024-11036-2"},{"key":"ref177","article-title":"Flower: A friendly federated learning research framework","author":"Beutel","year":"2020","journal-title":"arXiv:2007.14390"},{"key":"ref178","doi-asserted-by":"publisher","DOI":"10.12694\/scpe.v24i4.2220"},{"key":"ref179","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ac97d9"},{"key":"ref180","doi-asserted-by":"publisher","DOI":"10.3390\/e25111550"},{"key":"ref181","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2866697"},{"key":"ref182","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/3910250"},{"key":"ref183","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-79228-4_1"},{"key":"ref184","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref185","first-page":"7575","article-title":"CpSGD: Communication-efficient and differentially-private distributed SGD","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Agarwal"},{"key":"ref186","article-title":"Differentially private federated learning: A client level perspective","author":"Geyer","year":"2017","journal-title":"arXiv:1712.07557"},{"key":"ref187","first-page":"17455","article-title":"Differentially private learning with adaptive clipping","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Andrew"},{"key":"ref188","article-title":"Differentially private meta-learning","author":"Li","year":"2019","journal-title":"arXiv:1909.05830"},{"key":"ref189","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.128"},{"key":"ref190","article-title":"Protection against reconstruction and its applications in private federated learning","author":"Bhowmick","year":"2018","journal-title":"arXiv:1812.00984"},{"key":"ref191","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1700879"},{"key":"ref192","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2016.2535718"},{"key":"ref193","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2021.3058573"},{"key":"ref194","doi-asserted-by":"publisher","DOI":"10.1109\/ICAIIC57133.2023.10067038"},{"key":"ref195","doi-asserted-by":"publisher","DOI":"10.3390\/app12031764"},{"key":"ref196","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3196274"},{"key":"ref197","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/671"},{"key":"ref198","doi-asserted-by":"publisher","DOI":"10.3390\/app11167360"},{"key":"ref199","doi-asserted-by":"publisher","DOI":"10.1136\/BMJ.N71"},{"key":"ref200","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512252"},{"key":"ref201","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3379395"},{"key":"ref202","doi-asserted-by":"publisher","DOI":"10.3390\/math12131993"},{"key":"ref203","doi-asserted-by":"publisher","DOI":"10.1109\/ICRSS65752.2024.00055"},{"key":"ref204","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26235"},{"key":"ref205","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2022.3205665"},{"key":"ref206","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3352628"},{"key":"ref207","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"Brendan McMahan","year":"2016","journal-title":"arXiv:1602.05629"},{"key":"ref208","article-title":"Federated optimization: Distributed machine learning for on-device intelligence","author":"Kone\u010dn\u00fd","year":"2016","journal-title":"arXiv:1610.02527"},{"key":"ref209","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2017.2787987"},{"key":"ref210","article-title":"Learning differentially private recurrent language models","author":"Brendan McMahan","year":"2017","journal-title":"arXiv:1710.06963"},{"key":"ref211","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.03.061"},{"key":"ref212","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2019.2921755"},{"key":"ref213","first-page":"1","article-title":"Federated multi-task learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Smith"},{"key":"ref214","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2021.3082561"},{"key":"ref215","article-title":"Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption","author":"Hardy","year":"2017","journal-title":"arXiv:1711.10677"},{"key":"ref216","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3183326"},{"key":"ref217","first-page":"892","article-title":"Secure linear regression on vertically partitioned datasets","volume":"2016","author":"Gascn","year":"2016","journal-title":"IACR Cryptol. EPrint Arch."}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/11104099.pdf?arnumber=11104099","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T18:01:48Z","timestamp":1754503308000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11104099\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":217,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3593953","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}