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The case that different attributes\/features of the same instance are stored in different institutions is called vertically distributed data. The purpose of vertical\u2010federated feature selection (FS) is to reduce the feature dimension of vertical distributed data jointly without sharing local original data so that the feature subset obtained has the same or better performance as the original feature set. To solve this problem, in the paper, an embedded vertical\u2010federated FS algorithm based on particle swarm optimisation (PSO\u2010EVFFS) is proposed by incorporating evolutionary FS into the SecureBoost framework for the first time. By optimising both hyper\u2010parameters of the XGBoost model and feature subsets, PSO\u2010EVFFS can obtain a feature subset, which makes the XGBoost model more accurate. At the same time, since different participants only share insensitive parameters such as model loss function, PSO\u2010EVFFS can effectively ensure the privacy of participants' data. Moreover, an ensemble ranking strategy of feature importance based on the XGBoost tree model is developed to effectively remove irrelevant features on each participant. Finally, the proposed algorithm is applied to 10 test datasets and compared with three typical vertical\u2010federated learning frameworks and two variants of the proposed algorithm with different initialisation strategies. Experimental results show that the proposed algorithm can significantly improve the classification performance of selected feature subsets while fully protecting the data privacy of all participants.<\/jats:p>","DOI":"10.1049\/cit2.12122","type":"journal-article","created":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T03:49:45Z","timestamp":1657597785000},"page":"734-754","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["An embedded vertical\u2010federated feature selection algorithm based on particle swarm optimisation"],"prefix":"10.1049","volume":"8","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-0026-8181","authenticated-orcid":false,"given":"Yong","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering China University of Mining and Technology Xuzhou China"},{"name":"The Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of 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