{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T12:11:41Z","timestamp":1773317501034,"version":"3.50.1"},"reference-count":53,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001381","name":"National Research Foundation Singapore","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"publisher"}]},{"name":"AI Singapore Programme AISG","award":["AISG2-RP-2020-018"],"award-info":[{"award-number":["AISG2-RP-2020-018"]}]},{"DOI":"10.13039\/501100001381","name":"National Research Foundation Singapore","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Google PhD Fellowship"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Big Data"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1109\/tbdata.2022.3180117","type":"journal-article","created":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T20:12:56Z","timestamp":1654546376000},"page":"864-878","source":"Crossref","is-referenced-by-count":34,"title":["Practical Vertical Federated Learning With Unsupervised Representation Learning"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6463-0031","authenticated-orcid":false,"given":"Zhaomin","family":"Wu","sequence":"first","affiliation":[{"name":"National University of Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6539-6443","authenticated-orcid":false,"given":"Qinbin","family":"Li","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8618-4581","authenticated-orcid":false,"given":"Bingsheng","family":"He","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2021.3124599"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2021.3140131"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2936565"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57959-7"},{"key":"ref7","article-title":"Applied federated learning: Improving google keyboard query suggestions","author":"Yang","year":"2018"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.3001456"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2967670"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/mis.2021.3082561"},{"key":"ref11","first-page":"563","article-title":"VF2Boost: Very fast vertical federated gradient boosting for cross-enterprise learning","volume-title":"Proc. Int. Conf. Manage. Data","author":"Fu"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.14778\/3407790.3407811"},{"key":"ref13","article-title":"Split learning for health: Distributed deep learning without sharing raw patient data","author":"Vepakomma"},{"key":"ref14","article-title":"One-shot federated learning","author":"Guha","year":"2019"},{"key":"ref15","article-title":"Distilled one-shot federated learning","author":"Zhou","year":"2020"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/tit.2023.3344141"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref18","first-page":"517","article-title":"Unsupervised learning by predicting noise","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Bojanowski"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1561\/9781601988195"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2018.8486352"},{"key":"ref22","first-page":"6346","article-title":"Distributed learning without distress: Privacy-preserving empirical risk minimization","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Jayaraman"},{"key":"ref23","first-page":"8688","article-title":"Flame: Differentially private federated learning in the shuffle model","volume-title":"Proc. AAAI Conf. Artif. Intell.","author":"Liu"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11311"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/571"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3020955"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-79228-4_1"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CSF.2017.11"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1002\/nav.3800020109"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00176"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330765"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00065"},{"key":"ref34","first-page":"16937","article-title":"Inverting gradients\u2013How easy is it to break privacy in federated learning?","volume":"33","author":"Geiping","year":"2020"},{"key":"ref35","first-page":"2879","article-title":"Extremal mechanisms for local differential privacy","volume-title":"Adv. Neural Inf. Proc. Sys.","volume":"27","author":"Kairouz"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.14778\/3342263.3342274"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/1646396.1646452"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/2827872"},{"key":"ref39","article-title":"Asymmetrical vertical federated learning","author":"Liu","year":"2020"},{"key":"ref40","article-title":"VAFL: A method of vertical asynchronous federated learning","author":"Chen","year":"2020"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1994.374371"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2020.2992755"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17301"},{"key":"ref45","article-title":"Learning privately over distributed features: An ADMM sharing approach","author":"Hu","year":"2019"},{"key":"ref46","article-title":"A communication efficient collaborative learning framework for distributed features","author":"Liu","year":"2020"},{"key":"ref47","first-page":"16070","article-title":"Attack of the tails: Yes, you really can backdoor federated learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Wang","year":"2020"},{"key":"ref48","article-title":"Achieving differential privacy in vertically partitioned multiparty learning","author":"Xu","year":"2019"},{"key":"ref49","first-page":"973","article-title":"Empirical risk minimization in non-interactive local differential privacy revisited","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref50","article-title":"signSGD: Compressed optimisation for non-convex problems","author":"Bernstein","year":"2018"},{"key":"ref51","article-title":"Stochastic-sign SGD for federated learning with theoretical guarantees","author":"Jin","year":"2020"},{"key":"ref52","first-page":"3973","article-title":"FedBoost: A communication-efficient algorithm for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hamer"},{"key":"ref53","article-title":"Multi-participant multi-class vertical federated learning","author":"Feng","year":"2020"}],"container-title":["IEEE Transactions on Big Data"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6687317\/10750532\/09789268.pdf?arnumber=9789268","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T15:23:27Z","timestamp":1732721007000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9789268\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":53,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tbdata.2022.3180117","relation":{},"ISSN":["2332-7790","2372-2096"],"issn-type":[{"value":"2332-7790","type":"electronic"},{"value":"2372-2096","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12]]}}}