{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:18:59Z","timestamp":1791393539947,"version":"4.3.3"},"reference-count":39,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T00:00:00Z","timestamp":1678406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272362"],"award-info":[{"award-number":["62272362"]}],"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":["U19B2021"],"award-info":[{"award-number":["U19B2021"]}],"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":["ZCL21016"],"award-info":[{"award-number":["ZCL21016"]}],"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":["XJS220122"],"award-info":[{"award-number":["XJS220122"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Research Fund of Key Laboratory of Cryptography of Zhejiang Province","award":["62272362"],"award-info":[{"award-number":["62272362"]}]},{"name":"Open Research Fund of Key Laboratory of Cryptography of Zhejiang Province","award":["U19B2021"],"award-info":[{"award-number":["U19B2021"]}]},{"name":"Open Research Fund of Key Laboratory of Cryptography of Zhejiang Province","award":["ZCL21016"],"award-info":[{"award-number":["ZCL21016"]}]},{"name":"Open Research Fund of Key Laboratory of Cryptography of Zhejiang Province","award":["XJS220122"],"award-info":[{"award-number":["XJS220122"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["62272362"],"award-info":[{"award-number":["62272362"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["U19B2021"],"award-info":[{"award-number":["U19B2021"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["ZCL21016"],"award-info":[{"award-number":["ZCL21016"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["XJS220122"],"award-info":[{"award-number":["XJS220122"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>As a popular machine learning method, federated learning (FL) can effectively solve the issues of data silos and data privacy. However, traditional federated learning schemes cannot provide sufficient privacy protection. Furthermore, most secure federated learning schemes based on local differential privacy (LDP) ignore an important issue: they do not consider each client\u2019s differentiated privacy requirements. This paper introduces a perturbation algorithm (PDPM) that satisfies personalized local differential privacy (PLDP), resolving the issue of inadequate or excessive privacy protection for some participants due to the same privacy budget set for all clients. The algorithm enables clients to adjust the privacy parameters according to the sensitivity of their data, thus allowing the scheme to provide personalized privacy protection. To ensure the privacy of the scheme, we have conducted a strict privacy proof and simulated the scheme on both synthetic and real data sets. Experiments have demonstrated that our scheme is successful in producing high-quality models and fulfilling the demands of personalized privacy protection.<\/jats:p>","DOI":"10.3390\/e25030485","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T04:04:00Z","timestamp":1678680240000},"page":"485","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["PLDP-FL: Federated Learning with Personalized Local Differential Privacy"],"prefix":"10.3390","volume":"25","author":[{"given":"Xiaoying","family":"Shen","sequence":"first","affiliation":[{"name":"The State Key Laboratory of Integrated Service Networks, Xidian University, Xi\u2019an 710071, China"},{"name":"The Key Laboratory of Cryptography of Zhejiang Province, Hangzhou Normal University, Hangzhou 310030, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Jiang","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Integrated Service Networks, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yange","family":"Chen","sequence":"additional","affiliation":[{"name":"The School of Information Engineering, Xuchang University, Xuchang 461002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baocang","family":"Wang","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Integrated Service Networks, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Le","family":"Gao","sequence":"additional","affiliation":[{"name":"The Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,10]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Federated learning","volume":"13","author":"Yang","year":"2019","journal-title":"Synth. 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