Leveraging Learning Metrics for Improved Federated Learning
Andre Fu
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.