{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T17:01:17Z","timestamp":1793206877965,"version":"4.5.0"},"reference-count":33,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2019YFB2101805"],"award-info":[{"award-number":["2019YFB2101805"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Big Data"],"published-print":{"date-parts":[[2022,6,1]]},"DOI":"10.1109\/tbdata.2020.2992755","type":"journal-article","created":{"date-parts":[[2020,5,7]],"date-time":"2020-05-07T15:59:31Z","timestamp":1588867171000},"page":"843-854","source":"Crossref","is-referenced-by-count":134,"title":["Federated Forest"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8428-6039","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"first","affiliation":[{"name":"JD Digits, JD Intelligent Cities Business Unit, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingting","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijie","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Normal University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxuan","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Computing, National University of Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1995-5291","authenticated-orcid":false,"given":"Chuishi","family":"Meng","sequence":"additional","affiliation":[{"name":"JD Digits, JD Intelligent Cities Business Unit, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5947-1374","authenticated-orcid":false,"given":"Junbo","family":"Zhang","sequence":"additional","affiliation":[{"name":"JD Digits, JD Intelligent Cities Business Unit, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5224-4344","authenticated-orcid":false,"given":"Yu","family":"Zheng","sequence":"additional","affiliation":[{"name":"JD Digits, JD Intelligent Cities Business Unit, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1","article-title":"Regulation (eu) 2016\/679 of the european parliament and of the council of 27 april 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95\/46","volume":"59","author":"Regulation","year":"2016","journal-title":"Official J. Eur. Union"},{"key":"ref2","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":"ref3","article-title":"Federated learning: Strategies for improving communication efficiency","author":"Konen\u00fd","year":"2016"},{"key":"ref4","article-title":"Federated optimization: Distributed machine learning for on-device intelligence","author":"Konen\u00fd","year":"2016"},{"key":"ref5","volume-title":"Classification and Regression Trees","author":"Breiman","year":"1984"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/bf00058655"},{"key":"ref7","article-title":"Federated meta-learning with fast convergence and efficient communication","author":"Chen","year":"2018"},{"key":"ref8","first-page":"4424","article-title":"Federated multi-task learning","volume-title":"Proc. Annu. Conf. Neural Inf. Process. Syst.","author":"Smith"},{"key":"ref9","article-title":"Loadaboost: Loss-based adaboost federated machine learning on medical data","author":"Huang","year":"2018"},{"key":"ref10","article-title":"Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption","author":"Hardy","year":"2017"},{"key":"ref11","article-title":"LEAF: A benchmark for federated settings","author":"Caldas","year":"2018"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref13","article-title":"Secure federated transfer learning","author":"Liu","year":"2018"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2021.3082561"},{"key":"ref15","article-title":"Federated deep reinforcement learning","author":"Zhuo","year":"2019"},{"key":"ref16","article-title":"P3652.1 - guide for architectural framework and application of federated machine learning","author":"Group","year":"2019"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/11787006_1"},{"key":"ref18","first-page":"169","article-title":"On data banks and privacy homomorphisms","volume-title":"Foundations of Secure Computation","author":"Rivest","year":"1978"},{"key":"ref19","article-title":"Differentially private federated learning: A client level perspective","author":"Geyer","year":"2017"},{"key":"ref20","article-title":"Learning differentially private recurrent language models","volume-title":"Proc. Int. Conf. Learn. Representations","author":"McMahan"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2017.2787987"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-04212-7_30"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.2196\/medinform.8805"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1023\/A:I0I0933404324"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/335191.335438"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/11535706_11"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2013.43"},{"key":"ref28","article-title":"Privacy-preserving collaborative prediction using random forests","volume-title":"AMIA Summits Translational Sci. Proc.","volume":"2019","author":"Giacomelli"},{"key":"ref29","article-title":"Entity resolution and federated learning get a federated resolution","author":"Nock","year":"2018"},{"key":"ref30","article-title":"UCI machine learning repository","author":"Dua","year":"2017"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2018.10.022"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-23485-4_53"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.commatsci.2018.07.052"}],"container-title":["IEEE Transactions on Big Data"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6687317\/9773178\/09088965.pdf?arnumber=9088965","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T18:27:59Z","timestamp":1766687279000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9088965\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,1]]},"references-count":33,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tbdata.2020.2992755","relation":{},"ISSN":["2332-7790","2372-2096"],"issn-type":[{"value":"2332-7790","type":"electronic"},{"value":"2372-2096","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,1]]}}}