# Leveraging Learning Metrics for Improved Federated Learning

**Authors:** Andre Fu  
**arXiv:** [2309.00257](https://arxiv.org/abs/2309.00257)  
**Date:** 2023-09-01  
**Summary page:** https://www.summarizepaper.com/en/paper/2309.00257v1/

## Abstract

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.