FeadSeq: A Personalized Federated Anomaly Detection Framework for Discrete Event Sequences

Event sequence anomaly detection has garnered considerable attention in research, encompassing applications such as identifying anomalies in system logs, anomalous transaction users, etc. Yet, prevailing anomaly detection methods often rely solely on local data for training, potentially leading to imperfect detection performance. In this article, we introduce a personalized Fe derated a nomaly d etection framework for discrete event Seq uences, named FeadSeq. Specifically, we propose a separate architecture for sequence reconstruction networks (SEPRE) which partitions the network into two parts: a shared part and a standalone part, better suited for federated learning schemes. In tandem, we propose a novel partial shared federated learning scheme that employs a mask strategy to alleviate communication overhead and produce personalized local models to address the statistical heterogeneity of data among clients. This scheme dictates that a subset of weights is communicated between clients and servers for collaborative training, while the remaining weights are trained exclusively locally. To evaluate the effectiveness of FeadSeq, we conduct extensive experiments on both system logs and business process event logs. The results affirm the superiority of FeadSeq over existing personalized federated learning algorithms, showcasing not only improved performance but also reduced communication overhead.