Citation

Lim, Le Hang and Ong, Lee Yeng and Leow, Meng Chew (2025) Federated Learning for Anomaly Detection: A Systematic Review on Scalability, Adaptability, and Benchmarking Framework. Future Internet, 17 (8). p. 375. ISSN 1999-5903

Text Federated Learning for Anomaly Detection_ A Systematic Review on Scalability, Adaptability, and Benchmarking Framework.pdf - Published Version Restricted to Repository staff only Download (623kB)

Abstract

Anomaly detection plays an increasingly important role in maintaining the stability and reliability of modern distributed systems. Federated Learning (FL) is an emerging method that shows strong potential in enabling anomaly detection across decentralised environments. However, there are some crucial and tricky research challenges that remain unresolved, such as ensuring scalability, adaptability to dynamic server clusters, and the development of standardised evaluation frameworks for FL. This review aims to address the research gaps through a comprehensive analysis of existing studies. In this paper, a systematic review is conducted by covering three main aspects of the application of FL in anomaly detection: the impact of communication overhead towards scalability and real-time performance, the adaptability of FL frameworks to dynamic server clusters, and the key components required for a standardised benchmarking framework of FL-based anomaly detection. There are a total of 43 relevant articles, published between 2020 and 2025, which were selected from IEEE Xplore, Scopus, and ArXiv. The research findings highlight the potential of asynchronous updates and selective update mechanisms in improving FL’s real-time performance and scalability. This review primarily focuses on anomaly detection tasks in distributed system environments, such as network traffic analysis, IoT devices, and industrial monitoring, rather than domains like computer vision or financial fraud detection. While FL frameworks can handle dynamic client changes, the problem of data heterogeneity among the clients remains a significant obstacle that affects the model convergence speed. Moreover, the lack of a unified benchmarking framework to evaluate the performance of FL in anomaly detection poses a challenge to fair comparisons among the experimental results

Item Type:	Article
Uncontrolled Keywords:	Anomaly detection
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Sep 2025 03:44
Last Modified:	05 Oct 2025 05:07
URII:	http://shdl.mmu.edu.my/id/eprint/14561

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