Federated Learning-Based Anomaly Detection with Isolation Forest in the IoT-Edge Continuum

Haolong Xiang; Xuyun Zhang; Xiaolong Xu; Amin Beheshti; Lianyong Qi; Yujie Hong; Wanchun Dou

doi:10.1145/3702995

journal article Jan 12, 2026

Federated Learning-Based Anomaly Detection with Isolation Forest in the IoT-Edge Continuum

Haolong Xiang

ACM Transactions on Multimedia Computing, Communications, and Applications Vol. 22 No. 1 pp. 1-19 · Association for Computing Machinery (ACM)

View at Publisher Save 10.1145/3702995

Abstract

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.

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Details

Published: Jan 12, 2026
Vol/Issue: 22(1)
Pages: 1-19

Authors

H

Haolong Xiang

Nanjing University of Information Science and Technology, Nanjing, China

X

Xuyun Zhang

Macquarie University, Sydney, Australia

X

Xiaolong Xu

Nanjing University of Information Science and Technology, Nanjing, China

A

Amin Beheshti

Macquarie University, Sydney, Australia

L

Lianyong Qi

China University of Petroleum (East China), Qingdao, China

Y

Yujie Hong

China Merchants Bank, Shenzhen, China

W

Wanchun Dou

Nanjing University, Nanjing, China

Funding

National Natural Science Foundation of China Award: 92267104 and 62372242

Australian Government

Jiangsu Provincial Major Project on Basic Research of Cutting-edge and Leading Technologies Award: BK20232032

Cite This Article

Haolong Xiang, Xuyun Zhang, Xiaolong Xu, et al. (2026). Federated Learning-Based Anomaly Detection with Isolation Forest in the IoT-Edge Continuum. ACM Transactions on Multimedia Computing, Communications, and Applications, 22(1), 1-19. https://doi.org/10.1145/3702995

Federated Learning-Based Anomaly Detection with Isolation Forest in the IoT-Edge Continuum

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