  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.
