  The emergence of federated learning (FL) presents a promising approach to
leverage decentralized data while preserving privacy. Furthermore, the
combination of FL and anomaly detection is particularly compelling because it
allows for detecting rare and critical anomalies (usually also rare in locally
gathered data) in sensitive data from multiple sources, such as cybersecurity
and healthcare. However, benchmarking the performance of anomaly detection
methods in FL environments remains an underexplored area. This paper introduces
FedAD-Bench, a unified benchmark for evaluating unsupervised anomaly detection
algorithms within the context of FL. We systematically analyze and compare the
performance of recent deep learning anomaly detection models under federated
settings, which were typically assessed solely in centralized settings.
FedAD-Bench encompasses diverse datasets and metrics to provide a holistic
evaluation. Through extensive experiments, we identify key challenges such as
model aggregation inefficiencies and metric unreliability. We present insights
into FL's regularization effects, revealing scenarios in which it outperforms
centralized approaches due to its inherent ability to mitigate overfitting. Our
work aims to establish a standardized benchmark to guide future research and
development in federated anomaly detection, promoting reproducibility and fair
comparison across studies.
