{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T11:30:07Z","timestamp":1762342207783},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684444","type":"print"},{"value":"9781643684451","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>Federated learning is a mechanism for coordinating multiple participants to train models in response to the development of big data and artificial intelligence technologies. Federated learning not only introduces a large number of parameter exchange processes, but is also subject to attacks involving untrustworthy devices, so stronger privacy means are urgently needed to protect the data held by the parties. This paper briefly introduces the architecture and types of federated learning, analyzes the privacy risks and attack strategies, and summarizes the privacy protection technologies. By applying these technologies, this paper proposes a privacy protection scheme from three levels: center, local, local &amp; central.<\/jats:p>","DOI":"10.3233\/faia230826","type":"book-chapter","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:54:10Z","timestamp":1701446050000},"source":"Crossref","is-referenced-by-count":1,"title":["Research of Privacy-Preserving in Federated Learning"],"prefix":"10.3233","author":[{"given":"Xiaojing","family":"Yang","sequence":"first","affiliation":[{"name":"College of Computer Science, Xi\u2019an Shiyou University Xi\u2019an 710065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weijuan","family":"Min","sequence":"additional","affiliation":[{"name":"The 20th Research Institute of China Electronics Technology Group Corporation Xi\u2019an 710065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Advances in Artificial Intelligence, Big Data and Algorithms"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230826","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:54:12Z","timestamp":1701446052000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230826"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"ISBN":["9781643684444","9781643684451"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230826","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,30]]}}}