  Network intrusion detection systems are evolving into intelligent systems
that perform data analysis while searching for anomalies in their environment.
Indeed, the development of deep learning techniques paved the way to build more
complex and effective threat detection models. However, training those models
may be computationally infeasible in most Edge or IoT devices. Current
approaches rely on powerful centralized servers that receive data from all
their parties - violating basic privacy constraints and substantially affecting
response times and operational costs due to the huge communication overheads.
To mitigate these issues, Federated Learning emerged as a promising approach,
where different agents collaboratively train a shared model, without exposing
training data to others or requiring a compute-intensive centralized
infrastructure. This work presents GowFed, a novel network threat detection
system that combines the usage of Gower Dissimilarity matrices and Federated
averaging. Different approaches of GowFed have been developed based on state-of
the-art knowledge: (1) a vanilla version; and (2) a version instrumented with
an attention mechanism. Furthermore, each variant has been tested using
simulation oriented tools provided by TensorFlow Federated framework. In the
same way, a centralized analogous development of the Federated systems is
carried out to explore their differences in terms of scalability and
performance - across a set of designed experiments/scenarios. Overall, GowFed
intends to be the first stepping stone towards the combined usage of Federated
Learning and Gower Dissimilarity matrices to detect network threats in
industrial-level networks.
