{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T02:30:17Z","timestamp":1784860217591,"version":"3.55.0"},"reference-count":193,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"7","license":[{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&#x0026;D Program of China","award":["2022ZD0160504"],"award-info":[{"award-number":["2022ZD0160504"]}]},{"name":"Tsinghua-Toyota Joint Research Institute inter-disciplinary Program"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1109\/tkde.2024.3352628","type":"journal-article","created":{"date-parts":[[2024,1,26]],"date-time":"2024-01-26T19:00:03Z","timestamp":1706295603000},"page":"3615-3634","source":"Crossref","is-referenced-by-count":329,"title":["Vertical Federated Learning: Concepts, Advances, and Challenges"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3800-3533","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"first","affiliation":[{"name":"Institute for AI Industry Research, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2016-9503","authenticated-orcid":false,"given":"Yan","family":"Kang","sequence":"additional","affiliation":[{"name":"Webank, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6068-2635","authenticated-orcid":false,"given":"Tianyuan","family":"Zou","sequence":"additional","affiliation":[{"name":"Institute for AI Industry Research, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5019-0828","authenticated-orcid":false,"given":"Yanhong","family":"Pu","sequence":"additional","affiliation":[{"name":"Institute for AI Industry Research, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5468-6608","authenticated-orcid":false,"given":"Yuanqin","family":"He","sequence":"additional","affiliation":[{"name":"Webank, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4925-5907","authenticated-orcid":false,"given":"Xiaozhou","family":"Ye","sequence":"additional","affiliation":[{"name":"AsiaInfo Technologies, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6195-6415","authenticated-orcid":false,"given":"Ye","family":"Ouyang","sequence":"additional","affiliation":[{"name":"AsiaInfo Technologies, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4515-6212","authenticated-orcid":false,"given":"Ya-Qin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute for AI Industry Research, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5059-8360","authenticated-orcid":false,"given":"Qiang","family":"Yang","sequence":"additional","affiliation":[{"name":"Webank, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-01585-4"},{"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","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3387107"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC44109.2020.9175344"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1561\/9781680837896"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3124599"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106854"},{"issue":"226","key":"ref9","first-page":"1","article-title":"Fate: An industrial grade platform for collaborative learning with data protection","volume":"22","author":"Liu","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"ref10","article-title":"PyVertical: A vertical federated learning framework for multi-headed splitNN","author":"Romanini","year":"2021"},{"key":"ref12","article-title":"FedML: A research library and benchmark for federated machine learning","author":"He","year":"2020"},{"key":"ref13","article-title":"FedTree: A federated learning system for trees","volume-title":"Proc. Mach. Learn. Syst.","volume":"5","author":"Li","year":"2023"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3160699"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICTC55196.2022.9952628"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2022.3198176"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2021.3082561"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ACSSC.2018.8645549"},{"key":"ref19","article-title":"Secure linear regression on vertically partitioned datasets","volume":"2016","author":"Gasc\u00f3n","year":"2016","journal-title":"IACR Cryptol. ePrint Arch."},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014139"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467210"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/11731139_74"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3474369.3486872"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2020.2988525"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330765"},{"key":"ref26","first-page":"1397","article-title":"Label inference attacks against vertical federated learning","volume-title":"Proc. 31st USENIX Secur. Symp.","author":"Fu"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1017\/9781108966559.020"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.2298\/CSIS190923022O"},{"key":"ref29","first-page":"994","article-title":"CAFE: Catastrophic data leakage in vertical federated learning","volume":"34","author":"Jin","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref30","article-title":"RVFR: Robust vertical federated learning via feature subspace recovery","volume-title":"Proc. NeurIPS Workshop New Front. Federated Learn.","author":"Liu"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/tbdata.2022.3192121"},{"key":"ref32","article-title":"SplitNN-driven vertical partitioning","author":"Ceballos","year":"2020"},{"key":"ref33","first-page":"21087","article-title":"A coupled design of exploiting record similarity for practical vertical federated learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wu"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281275"},{"key":"ref35","article-title":"SecureBoost: A high performance gradient boosting tree framework for large scale vertical federated learning","author":"Chen","year":"2021"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9006000"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482361"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/tdsc.2023.3276365"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.14778\/3565816.3565823"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3523061"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.1995.598994"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2023.3309701"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2022.3178443"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/satml59370.2024.00029"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.14778\/3547305.3547316"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICARCV50220.2020.9305383"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/tbdata.2022.3192898"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/GCWkshps56602.2022.10008685"},{"key":"ref49","article-title":"VAFL: A method of vertical asynchronous federated learning","volume-title":"Proc. ICML Workshop Federated Learn. User Privacy Data Confidentiality","author":"Chen"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3072238"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467169"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17301"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403298"},{"key":"ref54","article-title":"FedGBF: An efficient vertical federated learning framework via gradient boosting and bagging","author":"Han","year":"2022"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457241"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/tbdata.2022.3180117"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.2196\/26598"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.3390\/a15080273"},{"key":"ref59","article-title":"Efficient batch homomorphic encryption for vertically federated XGBoost","author":"Xu","year":"2021"},{"key":"ref60","first-page":"2738","article-title":"Compressed-VFL: Communication-efficient learning with vertically partitioned data","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Castiglia"},{"key":"ref61","first-page":"29566","article-title":"Coresets for vertical federated learning: Regularized linear regression and k-means clustering","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Huang"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM53939.2023.10228895"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ICC45855.2022.9838917"},{"key":"ref64","first-page":"3757","article-title":"LESS-VFL: Communication-efficient feature selection for vertical federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Castiglia"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1145\/3588961"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118097"},{"key":"ref67","article-title":"Feature-distributed SVRG for high-dimensional linear classification","author":"Zhang","year":"2018"},{"key":"ref68","first-page":"517","article-title":"Unsupervised learning by predicting noise","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Bojanowski"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1145\/3510587"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9413697"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2022.3192050"},{"key":"ref72","article-title":"VFed-SSD: Towards practical vertical federated advertising","volume-title":"Proc. Int. Workshop Trustworthy Federated Learn. Conjunction IJCAI","author":"Li"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/tbdata.2024.3403386\/mm1"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-11748-0_4"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1145\/3510031"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1145\/3529836.3529904"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583874"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1145\/3501817"},{"key":"ref79","article-title":"Vertical semi-federated learning for efficient online advertising","volume-title":"Proc. Int. Workshop Trustworthy Federated Learn. Conjunction IJCAI","author":"Li"},{"key":"ref80","article-title":"Multi-participant multi-class vertical federated learning","author":"Feng","year":"2020"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109384"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9006280"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/tbdata.2022.3188292"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1061\/AJRUA6.0001058"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539402"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-32009-5_38"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-25952-7_6"},{"key":"ref88","first-page":"797","article-title":"Faster private set intersection based on OT extension","volume-title":"Proc. 23rd USENIX Secur. Symp.","author":"Pinkas"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/6692061"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/TrustCom50675.2020.00098"},{"key":"ref91","article-title":"Asymmetrical vertical federated learning","author":"Liu","year":"2020"},{"key":"ref92","article-title":"Vertical federated learning without revealing intersection membership","author":"Sun","year":"2021"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2022.3208630"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00023"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1145\/3359789.3359824"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.2478\/popets-2022-0045"},{"key":"ref97","article-title":"Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption","author":"Hardy","year":"2017"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.14778\/3407790.3407811"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3526127"},{"key":"ref100","article-title":"Federated logistic regression"},{"key":"ref101","article-title":"Label leakage and protection in two-party split learning","author":"Li","year":"2021"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1109\/BigCom57025.2022.00051"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1109\/SaTML54575.2023.00020"},{"key":"ref104","article-title":"Label leakage and protection from forward embedding in vertical federated learning","author":"Sun","year":"2022"},{"key":"ref105","article-title":"Feature reconstruction attacks and countermeasures of DNN training in vertical federated learning","author":"Ye","year":"2022"},{"key":"ref106","article-title":"Privacy leakage of real-world vertical federated learning","author":"Weng","year":"2020"},{"key":"ref107","article-title":"Is vertical logistic regression privacy-preserving? a comprehensive privacy analysis and beyond","author":"Hu","year":"2022"},{"key":"ref108","article-title":"Overlearning reveals sensitive attributes","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Song"},{"key":"ref109","article-title":"Parallel distributed logistic regression for vertical federated learning without third-party coordinator","volume-title":"Proc. Workshop Federated Mach. Learn. User Privacy Data Confidentiality","author":"Yang"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1109\/MIC.2021.3138853"},{"key":"ref111","article-title":"FedSGC: Federated simple graph convolution for node classification","volume-title":"Proc. Int. Joint Conf. Artif. Intell. Workshops","author":"Tsz-Him Cheung"},{"key":"ref112","article-title":"Additively homomorphical encryption based deep neural network for asymmetrically collaborative machine learning","author":"Zhang","year":"2020"},{"key":"ref113","article-title":"Mitigating leakage in federated learning with trusted hardware","volume-title":"Proc. Privacy Preserving Mach. Learn. Workshop NeurIPS","author":"Chamani"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1561\/2200000035"},{"key":"ref115","article-title":"Making split learning resilient to label leakage by potential energy loss","author":"Zheng","year":"2022"},{"key":"ref116","doi-asserted-by":"crossref","DOI":"10.1109\/BigCom57025.2022.00051","article-title":"Residue-based label protection mechanisms in vertical logistic regression","author":"Tan","year":"2022"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/418"},{"key":"ref118","article-title":"Defending against reconstruction attack in vertical federated learning","author":"Sun","year":"2021"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.1109\/MLHPC.2016.004"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1045"},{"key":"ref121","article-title":"Deep gradient compression: Reducing the communication bandwidth for distributed training","author":"Lin","year":"2017"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813687"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_2"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-79228-4_1"},{"key":"ref125","article-title":"Hybrid differentially private federated learning on vertically partitioned data","author":"Wang","year":"2020"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/272"},{"key":"ref127","first-page":"513","article-title":"AttriGuard: A practical defense against attribute inference attacks via adversarial machine learning","volume-title":"Proc. 27th USENIX Secur. Symp.","author":"Jia"},{"key":"ref128","article-title":"Backdoor attacks and defenses in feature-partitioned collaborative learning","author":"Liu","year":"2020"},{"key":"ref129","article-title":"Attacking vertical collaborative learning system using adversarial dominating inputs","author":"Pang","year":"2022"},{"key":"ref130","article-title":"Explaining and harnessing adversarial examples","volume":"1050","author":"Goodfellow","year":"2015","journal-title":"Stat"},{"key":"ref131","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2022.3161016"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_11"},{"key":"ref133","doi-asserted-by":"publisher","DOI":"10.1145\/3501811"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9006179"},{"key":"ref135","article-title":"Interpret federated learning with shapley values","author":"Wang","year":"2019"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.4682439"},{"key":"ref137","article-title":"Fair and efficient contribution valuation for vertical federated learning","author":"Fan","year":"2022"},{"key":"ref138","first-page":"2088","article-title":"VF-PS: How to select important participants in vertical federated learning, efficiently and securely?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Jiang"},{"key":"ref139","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2022.102474"},{"key":"ref140","article-title":"A vertical federated learning method for interpretable scorecard and its application in credit scoring","author":"Zheng","year":"2020"},{"key":"ref141","first-page":"7852","article-title":"Fairvfl: A fair vertical federated learning framework with contrastive adversarial learning","volume":"35","author":"Qi","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref142","article-title":"Achieving model fairness in vertical federated learning","author":"Liu","year":"2021"},{"key":"ref143","first-page":"202","article-title":"Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid","volume-title":"Proc. 2nd Int. Conf. Knowl. Discov. Data Mining","author":"Kohavi"},{"key":"ref144","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2014.03.001"},{"key":"ref145","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2007.12.020"},{"key":"ref146","article-title":"Give me some credit dataset"},{"key":"ref147","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.35"},{"key":"ref148","doi-asserted-by":"publisher","DOI":"10.1117\/12.148698"},{"key":"ref149","first-page":"261","article-title":"Using the ADAP learning algorithm to forecast the onset of diabetes mellitus","volume-title":"Proc. Annu. Symp. Comput. Appl. Med. Care","author":"Smith"},{"key":"ref150","article-title":"Avazu dataset"},{"key":"ref151","article-title":"Criteo dataset"},{"key":"ref152","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2004.03.020"},{"key":"ref153","article-title":"UCI machine learning repository","author":"Dua","year":"2017"},{"key":"ref154","doi-asserted-by":"publisher","DOI":"10.1016\/S0168-1699(99)00046-0"},{"key":"ref155","doi-asserted-by":"publisher","DOI":"10.1145\/1646396.1646452"},{"issue":"4","key":"ref156","first-page":"381","article-title":"Handwritten digit recognition by combined classifiers","volume":"34","author":"Duin","year":"1998","journal-title":"Kybernetika"},{"key":"ref157","article-title":"Epsilon dataset"},{"key":"ref158","article-title":"Breast histopathology images","author":"Mooney"},{"key":"ref159","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301590"},{"key":"ref160","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298801"},{"key":"ref161","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009953814988"},{"key":"ref162","doi-asserted-by":"publisher","DOI":"10.1145\/276675.276685"},{"key":"ref163","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref164","article-title":"Yahoo answers dataset","author":"Rakshit"},{"issue":"3","key":"ref165","first-page":"341","article-title":"A modified finite newton method for fast solution of large scale linear svms","volume":"6","author":"Keerthi","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"ref166","article-title":"Optimizing privacy, utility and efficiency in constrained multi-objective federated learning","author":"Kang","year":"2023"},{"key":"ref167","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM48099.2022.10001533"},{"key":"ref168","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2022.3142374"},{"key":"ref169","doi-asserted-by":"publisher","DOI":"10.1145\/3447380"},{"key":"ref170","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109881"},{"key":"ref171","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-75765-6_28"},{"key":"ref172","first-page":"10524","article-title":"Exploiting data sparsity in secure cross-platform social recommendation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Cui"},{"key":"ref173","doi-asserted-by":"publisher","DOI":"10.1109\/EIECS53707.2021.9587935"},{"key":"ref174","doi-asserted-by":"publisher","DOI":"10.1109\/ICWS55610.2022.00054"},{"key":"ref175","article-title":"Bytedance breaks federated learning: Open source fedlearner framework, 209% increase in advertising efficiency","author":"Cai","year":"2021"},{"key":"ref176","article-title":"JDs exploration and practice of large-scale federated learning","author":"Hou","year":"2021"},{"key":"ref177","article-title":"The practice of federated learning in tencent wesee advertising","author":"Lin","year":"2021"},{"key":"ref178","article-title":"Huaweis exploration and application in federated advertising algorithm","author":"Wu","year":"2022"},{"key":"ref179","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_17"},{"key":"ref180","article-title":"CONVINCED\u2014Enabling privacy-preserving survival analyses using multi-party computation","author":"Rooijakkers","year":"2020"},{"key":"ref181","doi-asserted-by":"publisher","DOI":"10.1109\/ICCT52962.2021.9657870"},{"key":"ref182","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3148997"},{"key":"ref183","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.1900461"},{"key":"ref184","doi-asserted-by":"publisher","DOI":"10.1109\/GCWkshps50303.2020.9367398"},{"key":"ref185","doi-asserted-by":"publisher","DOI":"10.1109\/ECOC52684.2021.9605846"},{"key":"ref186","article-title":"A federated learning framework for smart grids: Securing power traces in collaborative learning","author":"Liu","year":"2021"},{"key":"ref187","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-021-01775-2"},{"key":"ref188","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6824"},{"key":"ref189","article-title":"A vertical federated learning framework for graph convolutional network","author":"Ni","year":"2021"},{"key":"ref190","doi-asserted-by":"publisher","DOI":"10.1145\/3595185"},{"key":"ref191","article-title":"SecureBoost hyperparameter tuning via multi-objective federated learning","volume-title":"Proc. Int. Workshop Trustworthy Federated Learn. Conjunction IJCAI","author":"Ren"},{"key":"ref192","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3195956"},{"key":"ref193","article-title":"Taking human out of learning applications: A survey on automated machine learning","author":"Yao","year":"2018"},{"key":"ref194","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-020-00247-z"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/10549876\/10415268.pdf?arnumber=10415268","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T13:20:12Z","timestamp":1719408012000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10415268\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7]]},"references-count":193,"journal-issue":{"issue":"7"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2024.3352628","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"value":"1041-4347","type":"print"},{"value":"1558-2191","type":"electronic"},{"value":"2326-3865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7]]}}}