Reliable and Secure Anomaly Detection in Heterogeneous Federated Learning: A Comprehensive Review

Anomaly detection plays a critical role in ensuring the security of data and systems across diverse real-world applications. Traditional anomaly detection relies on collecting large datasets on a central server, but in reality, data are often spread across different clients and cannot be directly shared due to privacy concerns. Federated learning (FL) has thus emerged as a promising framework for privacy-preserving anomaly detection by enabling collaborative model training without exposing raw data. However, a major challenge in FL-based anomaly detection (FLAD) is heterogeneity, i.e., clients often have data with different distributions, feature spaces, and resource constraints. This makes it difficult to build accurate and reliable anomaly detection models. While some surveys have explored aspects, such as privacy protection, anomaly detection techniques, or FL methodologies, a comprehensive review of privacy-preserving anomaly detection within heterogeneous FL settings remains lacking. This paper systematically review and categorize anomaly detection methods designed for secure and reliable use in heterogeneous FL, considering both data and client heterogeneity. Finally, we highlight future research directions, aiming to guide further progress and support the wider adoption of FLAD in real-world scenarios.