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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2026,1,31]]},"abstract":"<jats:p>Traditional methods for ensuring security and privacy face challenges in safeguarding multimedia data within the IoT-edge continuum, as their significant computational demands render them unsuitable for IoT devices with limited resources. Next, we find that the federated learning techique can naturally adapt to edge frameworks and provide effective data security and privacy protection. In this article, we propose FLiForest, an innovative anomaly detection approach that integrates federated learning with the isolation forest algorithm, tailored for the IoT-edge continuum. Specifically, our method designs a three-stage process, including data collection and sampling, model training, and data testing, to joint-train an isolation forest among clients and edge servers. FLiForest facilitates decentralized model training across IoT devices, enhancing data privacy and reducing computational burden, without necessitating the exchange of multimedia data. Through extensive experiments on a variety of multimedia datasets, the efficacy of our method is benchmarked against the state-of-the-art anomaly detection methods, showcasing its superior detection accuracy and robustness in ensuring data privacy and security.<\/jats:p>","DOI":"10.1145\/3702995","type":"journal-article","created":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T11:46:04Z","timestamp":1730547964000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":38,"title":["Federated Learning-Based Anomaly Detection with Isolation Forest in the IoT-Edge Continuum"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4565-8829","authenticated-orcid":false,"given":"Haolong","family":"Xiang","sequence":"first","affiliation":[{"name":"Nanjing University of Information Science and Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7353-4159","authenticated-orcid":false,"given":"Xuyun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Macquarie University, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4879-9803","authenticated-orcid":false,"given":"Xiaolong","family":"Xu","sequence":"additional","affiliation":[{"name":"Nanjing University of Information Science and Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5988-5494","authenticated-orcid":false,"given":"Amin","family":"Beheshti","sequence":"additional","affiliation":[{"name":"Macquarie University, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9875-9856","authenticated-orcid":false,"given":"Lianyong","family":"Qi","sequence":"additional","affiliation":[{"name":"China University of Petroleum (East China), Qingdao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7536-6547","authenticated-orcid":false,"given":"Yujie","family":"Hong","sequence":"additional","affiliation":[{"name":"China Merchants Bank, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4833-2023","authenticated-orcid":false,"given":"Wanchun","family":"Dou","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,1,12]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/s22114133"},{"key":"e_1_3_2_3_2","doi-asserted-by":"crossref","unstructured":"Luiz Bittencourt Roger Immich Rizos Sakellariou Nelson Fonseca Edmundo Madeira Marilia Curado Leandro Villas Luiz DaSilva Craig Lee and Omer Rana. 2018. 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