Federated Learning for Anomaly Detection: A Systematic Review on Scalability, Adaptability, and Benchmarking Framework

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 crucia...

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書誌詳細

出版年:	Future Internet
主要な著者:	Le-Hang Lim, Lee-Yeng Ong, Meng-Chew Leow
フォーマット:	論文
言語:	英語
出版事項:	MDPI AG 2025-08-01
主題:	federated learning anomaly detection scalability real-time performance dynamic server clusters benchmarking framework
オンライン･アクセス:	https://www.mdpi.com/1999-5903/17/8/375