Support Vector Based Anomaly Detection in Federated Learning
This work addresses data privacy challenges in anomaly detection for domains like cybersecurity and industrial systems, though it appears incremental as it builds on existing federated learning and SVM methods.
The paper tackled anomaly detection in federated learning by introducing Ensemble SVDD and Support Vector Election algorithms, which showed promising initial results as alternatives to neural networks with lower computational costs and effectiveness on small datasets.
Anomaly detection plays a crucial role in various domains, from cybersecurity to industrial systems. However, traditional centralized approaches often encounter challenges related to data privacy. In this context, Federated Learning emerges as a promising solution. This work introduces two innovative algorithms--Ensemble SVDD and Support Vector Election--that leverage Support Vector Machines for anomaly detection in a federated setting. In comparison with the Neural Networks typically used in within Federated Learning, these new algorithms emerge as potential alternatives, as they can operate effectively with small datasets and incur lower computational costs. The novel algorithms are tested in various distributed system configurations, yielding promising initial results that pave the way for further investigation.