{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T08:38:55Z","timestamp":1770539935267,"version":"3.49.0"},"reference-count":45,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:00:00Z","timestamp":1654819200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:00:00Z","timestamp":1654819200000},"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":[],"published-print":{"date-parts":[[2022,6,10]]},"DOI":"10.1109\/iwqos54832.2022.9812909","type":"proceedings-article","created":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T19:27:44Z","timestamp":1657049264000},"page":"1-11","source":"Crossref","is-referenced-by-count":2,"title":["Privacy-Preserving and Robust Federated Deep Metric Learning"],"prefix":"10.1109","author":[{"given":"Yulong","family":"Tian","sequence":"first","affiliation":[{"name":"Nanjing University,State Key Laboratory for Novel Software Technology,China"}]},{"given":"Xiaopeng","family":"Ke","sequence":"additional","affiliation":[{"name":"Nanjing University,State Key Laboratory for Novel Software Technology,China"}]},{"given":"Zeyi","family":"Tao","sequence":"additional","affiliation":[{"name":"College of William & Mary,Department of Computer Science,USA"}]},{"given":"Shaohua","family":"Ding","sequence":"additional","affiliation":[{"name":"Nanjing University,State Key Laboratory for Novel Software Technology,China"}]},{"given":"Fengyuan","family":"Xu","sequence":"additional","affiliation":[{"name":"Nanjing University,State Key Laboratory for Novel Software Technology,China"}]},{"given":"Qun","family":"Li","sequence":"additional","affiliation":[{"name":"College of William & Mary,Department of Computer Science,USA"}]},{"given":"Hao","family":"Han","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics,College of Computer Science and Technology,China"}]},{"given":"Sheng","family":"Zhong","sequence":"additional","affiliation":[{"name":"Nanjing University,State Key Laboratory for Novel Software Technology,China"}]},{"given":"Xinyi","family":"Fu","sequence":"additional","affiliation":[{"name":"Ant Group,China"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Revisiting training strategies and generalization performance in deep metric learning","author":"roth","year":"2020","journal-title":"ICML"},{"key":"ref38","article-title":"On the fenchel duality between strong convexity and lipschitz continuous gradient","author":"zhou","year":"2018"},{"key":"ref33","article-title":"Towards federated learning at scale: System design","author":"bonawitz","year":"2019"},{"key":"ref32","article-title":"Private machine learning in tensorflow using secure computation","author":"dahl","year":"2018"},{"key":"ref31","article-title":"A generic framework for privacy preserving deep learning","author":"ryffel","year":"2018"},{"key":"ref30","article-title":"A blockchain-orchestrated federated learning architecture for healthcare consortia","author":"passerat-palmbach","year":"2019"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220106"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00065"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134012"},{"key":"ref34","article-title":"A hybrid deep learning architecture for privacy-preserving mobile analytics","author":"osia","year":"2017"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/FG.2018.00020"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24261-3_7"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.149"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.07.007"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2717505"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298609"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46448-0_44"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01270-0_12"},{"key":"ref18","article-title":"General data protection regulation","year":"2018"},{"key":"ref19","article-title":"Federated learning for mobile keyboard prediction","author":"hard","year":"2018"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761267"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2017.02.006"},{"key":"ref27","article-title":"Intel software guard extensions for linux","year":"2016"},{"key":"ref3","article-title":"Privacy-preserving deep learning","author":"shokri","year":"2015","journal-title":"CCS"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CSCI49370.2019.00218"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2017.2787987"},{"key":"ref8","article-title":"Federated learning for wireless communications: Motivation, opportunities and challenges","author":"niknam","year":"2019"},{"key":"ref7","article-title":"Split learning for health: Distributed deep learning without sharing raw patient data","author":"vepakomma","year":"2018"},{"key":"ref2","article-title":"Federated learning: Collaborative machine learning without centralized training data","author":"mcmahan","year":"2017","journal-title":"Google Research Blog"},{"key":"ref9","article-title":"Advances and open problems in federated learning","author":"kairouz","year":"2019"},{"key":"ref1","article-title":"Federated learning of deep networks using model averaging","author":"mcmahan","year":"2016"},{"key":"ref20","article-title":"Applied federated learning: Improving google keyboard query suggestions","author":"yang","year":"2018"},{"key":"ref45","article-title":"In defense of the triplet loss for person re-identification","author":"hermans","year":"2017"},{"key":"ref22","article-title":"Secure federated transfer learning","author":"liu","year":"2018"},{"key":"ref21","article-title":"Federated meta-learning for recommendation","author":"chen","year":"2018"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/2382196.2382307"},{"key":"ref24","article-title":"Secureboost: A lossless federated learning framework","author":"cheng","year":"2019"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref23","article-title":"Federated reinforcement learning","author":"zhuo","year":"2019"},{"key":"ref44","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"AISTATS"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1561\/0400000042"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2012.03.010"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"}],"event":{"name":"2022 IEEE\/ACM 30th International Symposium on Quality of Service (IWQoS)","location":"Oslo, Norway","start":{"date-parts":[[2022,6,10]]},"end":{"date-parts":[[2022,6,12]]}},"container-title":["2022 IEEE\/ACM 30th International Symposium on Quality of Service (IWQoS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9812862\/9812863\/09812909.pdf?arnumber=9812909","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T00:44:46Z","timestamp":1659660286000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9812909\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,10]]},"references-count":45,"URL":"https:\/\/doi.org\/10.1109\/iwqos54832.2022.9812909","relation":{},"subject":[],"published":{"date-parts":[[2022,6,10]]}}}