  Currently in the federated setting, no learning schemes leverage the emerging
research of explainable artificial intelligence (XAI) in particular the novel
learning metrics that help determine how well a model is learning. One of these
novel learning metrics is termed `Effective Rank' (ER) which measures the
Shannon Entropy of the singular values of a matrix, thus enabling a metric
determining how well a layer is mapping. By joining federated learning and the
learning metric, effective rank, this work will \textbf{(1)} give the first
federated learning metric aggregation method \textbf{(2)} show that effective
rank is well-suited to federated problems by out-performing baseline Federated
Averaging \cite{konevcny2016federated} and \textbf{(3)} develop a novel
weight-aggregation scheme relying on effective rank.
