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Syst."],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:p>\n            As an advanced carrier of on-board sensors, connected autonomous vehicle (CAV) can be viewed as an aggregation of self-adaptive systems with monitor-analyze-plan-execute (MAPE) for vehicle-related services. Meanwhile, machine learning (ML) has been applied to enhance analysis and plan functions of MAPE so that self-adaptive systems have optimal adaption to changing conditions. However, most of ML-based approaches don\u2019t utilize CAVs\u2019 connectivity to collaboratively generate an optimal learner for MAPE, because of sensor data threatened by gradient leakage attack (GLA). In this article, we first design an intelligent architecture for MAPE-based self-adaptive systems on web 3.0-based CAVs, in which a collaborative machine learner supports the capabilities of managing systems. Then, we observe by practical experiments that importance sampling of sparse vector technique (SVT) approaches cannot defend GLA well. Next, we propose a fine-grained SVT approach to secure the learner in MAPE-based self-adaptive systems that uses layer and gradient sampling to select uniform and important gradients. At last, extensive experiments show that our private learner spends a slight utility cost for MAPE (e.g.,\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(0.77\\%\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            decrease in accuracy) defending GLA and outperforms the typical SVT approaches in terms of defense (increased by\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(10\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            \u2013\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(14\\%\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            attack success rate) and utility (decreased by\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(1.29\\%\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            accuracy loss).\n          <\/jats:p>","DOI":"10.1145\/3690768","type":"journal-article","created":{"date-parts":[[2024,8,31]],"date-time":"2024-08-31T10:24:57Z","timestamp":1725099897000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Secure Collaborative Learning for Self-Adaptive Systems on Connected Autonomous Vehicles"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3262-9420","authenticated-orcid":false,"given":"Xiaotong","family":"Wu","sequence":"first","affiliation":[{"name":"Alibaba Business School, Hangzhou Normal University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4911-5744","authenticated-orcid":false,"given":"Yuwen","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7342-6083","authenticated-orcid":false,"given":"Xiaoxiao","family":"Chi","sequence":"additional","affiliation":[{"name":"Department of Computing, Macquarie University, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8873-6810","authenticated-orcid":false,"given":"Rong","family":"Jiang","sequence":"additional","affiliation":[{"name":"Yunnan Key Laboratory of Service Computing, Yunnan University of Finance and Economics, Kunming, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3488-4679","authenticated-orcid":false,"given":"Xiaokang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Faculty of Business Data Science, Kansai University, Osaka, Japan and RIKEN Center for Advanced Intelligence Project, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0162-6921","authenticated-orcid":false,"given":"Wajid","family":"Rafique","sequence":"additional","affiliation":[{"name":"Department of Electrical and Software Engineering, University of Calgary, Calgary, Alberta, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7656-0184","authenticated-orcid":false,"given":"Maqbool","family":"Khan","sequence":"additional","affiliation":[{"name":"Software Competence Center Hagenberg, Hagenberg, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,9,13]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1045"},{"key":"e_1_3_2_4_2","first-page":"15453","volume-title":"Proceedings of the Annual Conference on Neural Information Processing Systems (NeurIPS \u201919)","author":"Bagdasaryan Eugene","year":"2019","unstructured":"Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov. 2019. 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