  Sharing of telecommunication network data, for example, even at high
aggregation levels, is nowadays highly restricted due to privacy legislation
and regulations and other important ethical concerns. It leads to scattering
data across institutions, regions, and states, inhibiting the usage of AI
methods that could otherwise take advantage of data at scale. It creates the
need to build a platform to control such data, build models or perform
calculations. In this work, we propose an approach to building the bridge among
anomaly detection, federated learning, and data streams. The overarching goal
of the work is to detect anomalies in a federated environment over distributed
data streams. This work complements the state-of-the-art by adapting the data
stream algorithms in a federated learning setting for anomaly detection and by
delivering a robust framework and demonstrating the practical feasibility in a
real-world distributed deployment scenario.
