{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:51:40Z","timestamp":1783439500491,"version":"3.54.6"},"reference-count":48,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"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. on Mobile Comput."],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1109\/tmc.2025.3531919","type":"journal-article","created":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T19:09:13Z","timestamp":1737400153000},"page":"5264-5279","source":"Crossref","is-referenced-by-count":6,"title":["Utility-Enhanced Personalized Privacy Preservation in Hierarchical Federated Learning"],"prefix":"10.1109","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9331-3513","authenticated-orcid":false,"given":"Jianan","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Computer Science, Purdue University Indianapolis, Indianapolis, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6014-0396","authenticated-orcid":false,"given":"Honglu","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, Miami University, Oxford, OH, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8847-8345","authenticated-orcid":false,"given":"Qin","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Georgia State University, Atlanta, GA, USA"}],"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. Int. Conf. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148862"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3003744"},{"key":"ref4","article-title":"Hierarchical federated learning through LAN-WAN orchestration","author":"Yuan","year":"2020"},{"key":"ref5","article-title":"Hierarchically fair federated learning","author":"Zhang","year":"2020"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3112604"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2020.3018159"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00065"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2019.2897554"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.001.1900506"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00023"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00029"},{"key":"ref14","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.","author":"Geiping"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00989"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01607"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/11787006_1"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-79228-4_1"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1561\/0400000042"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2022.3168556"},{"key":"ref21","first-page":"1895","article-title":"Evaluating differentially private machine learning in practice","volume-title":"Proc. 28th USENIX Secur. Symp.","author":"Jayaraman"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2991416"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00063"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-17653-2_13"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9005465"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM46510.2021.9685644"},{"key":"ref27","article-title":"Learning differentially private recurrent language models","volume-title":"Proc. Int. Conf. Learn. Representations","author":"McMahan"},{"key":"ref28","article-title":"Differentially private federated learning: A client level perspective","author":"Geyer","year":"2017"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3338501.3357370"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.2988575"},{"key":"ref31","first-page":"2521","article-title":"Shuffled model of differential privacy in federated learning","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Girgis"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3037194"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.21203\/rs.3.rs-1005694\/v1"},{"key":"ref34","article-title":"Differential privacy-enabled federated learning for sensitive health data","author":"Choudhury","year":"2019"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2014.56"},{"key":"ref36","first-page":"90","article-title":"Efficient privacy-preserving biometric identification","volume-title":"Proc. 17th Conf. Netw. Distrib. System Secur. Symp.","author":"Evans"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.2307\/2283137"},{"key":"ref38","article-title":"Bit-aware randomized response for local differential privacy in federated learning","author":"Lai","year":"2022"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-17140-6_10"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3372224.3419188"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM46510.2021.9685786"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2020.3035770"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/217"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3299947"},{"key":"ref45","article-title":"Federated learning with non-IID data","author":"Zhao","year":"2018"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2981430"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1561\/2200000050"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/bigdata62323.2024.10826023"}],"container-title":["IEEE Transactions on Mobile Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/7755\/10990048\/10847868.pdf?arnumber=10847868","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T04:24:47Z","timestamp":1746678287000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10847868\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":48,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tmc.2025.3531919","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":[[2025,6]]}}}