  As next-generation networks materialize, increasing levels of intelligence
are required. Federated Learning has been identified as a key enabling
technology of intelligent and distributed networks; however, it is prone to
concept drift as with any machine learning application. Concept drift directly
affects the model's performance and can result in severe consequences
considering the critical and emergency services provided by modern networks. To
mitigate the adverse effects of drift, this paper proposes a concept drift
detection system leveraging the federated learning updates provided at each
iteration of the federated training process. Using dimensionality reduction and
clustering techniques, a framework that isolates the system's drifted nodes is
presented through experiments using an Intelligent Transportation System as a
use case. The presented work demonstrates that the proposed framework is able
to detect drifted nodes in a variety of non-iid scenarios at different stages
of drift and different levels of system exposure.
