{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T20:41:34Z","timestamp":1773002494964,"version":"3.50.1"},"reference-count":22,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T00:00:00Z","timestamp":1741046400000},"content-version":"vor","delay-in-days":62,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFB2703801"],"award-info":[{"award-number":["2023YFB2703801"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21A20463"],"award-info":[{"award-number":["U21A20463"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22B2027"],"award-info":[{"award-number":["U22B2027"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["L221014"],"award-info":[{"award-number":["L221014"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009967","name":"Xinjiang Production and Construction Corps","doi-asserted-by":"publisher","award":["CZ002702-04"],"award-info":[{"award-number":["CZ002702-04"]}],"id":[{"id":"10.13039\/501100009967","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>\n                    Existing research indicates that original federated learning is not absolutely secure; attackers can infer the original training data based on reconstructed gradient information. Therefore, we will further investigate methods to protect data privacy and prevent adversaries from reconstructing sensitive training samples from shared gradients. To achieve this, we propose a defense strategy called SLGD, which enhances model robustness by combining sparse learning and gradient perturbation techniques. The core idea of this approach consists of two parts. First, before processing training data at the RSU, we preprocess the data using sparse techniques to reduce data transmission and compress data size. Second, the strategy extracts feature representations from the model and performs gradient filtering based on the\n                    <jats:italic>l<\/jats:italic>\n                    <jats:sub>2<\/jats:sub>\n                    norm of this layer. Selected gradient values are then perturbed using Von Mises\u2013Fisher (VMF) distribution to obfuscate gradient information, thereby defending against gradient reconstruction attacks and ensuring model security. Finally, we validate the effectiveness and superiority of the proposed method across different datasets and attack scenarios.\n                  <\/jats:p>","DOI":"10.1155\/int\/9253392","type":"journal-article","created":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T22:31:53Z","timestamp":1741732313000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Gradient Reconstruction Protection Based on Sparse Learning and Gradient Perturbation in IoV"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2333-739X","authenticated-orcid":false,"given":"Jia","family":"Zhao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6413-7829","authenticated-orcid":false,"given":"Xinyu","family":"Rao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2109-7426","authenticated-orcid":false,"given":"Bokai","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7502-0064","authenticated-orcid":false,"given":"Yanchun","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6309-1741","authenticated-orcid":false,"given":"Jiaqi","family":"He","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0559-3873","authenticated-orcid":false,"given":"Hongliang","family":"Ma","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4706-4266","authenticated-orcid":false,"given":"Wenjia","family":"Niu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5974-1589","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2025,3,4]]},"reference":[{"key":"e_1_2_11_1_2","article-title":"A Survey on Federated Learning: a Perspective from Multi-Party Computation","volume":"18","author":"Liu F.","year":"2023","journal-title":"Frontiers of Computer Science"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813677"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"e_1_2_11_4_2","doi-asserted-by":"crossref","unstructured":"GanjuK. 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