  Federated Learning (FL) is a machine learning paradigm in which many clients
cooperatively train a single centralized model while keeping their data private
and decentralized. FL is commonly used in edge computing, which involves
placing computer workloads (both hardware and software) as close as possible to
the edge, where the data is being created and where actions are occurring,
enabling faster response times, greater data privacy, and reduced data transfer
costs. However, due to the heterogeneous data distributions/contents of
clients, it is non-trivial to accurately evaluate the contributions of local
models in global centralized model aggregation. This is an example of a major
challenge in FL, commonly known as data imbalance or class imbalance. In
general, testing and assessing FL algorithms can be a very difficult and
complex task due to the distributed nature of the systems. In this work, a
framework is proposed and implemented to assess FL algorithms in a more easy
and scalable way. This framework is evaluated over a distributed edge-like
environment managed by a container orchestration platform (i.e. Kubernetes).
