标题： 利用学习度量改进联邦学习 显示英文标题

标题： Leveraging Learning Metrics for Improved Federated Learning

摘要： 目前在联邦设置中，没有学习方案特别利用可解释人工智能（XAI）的新兴研究，尤其是帮助确定模型学习效果的新学习度量标准。 其中一种新的学习度量标准被称为“有效秩”（ER），它测量矩阵奇异值的香农熵，从而能够确定一个层映射效果的好坏。 通过将联邦学习和学习度量有效秩相结合，这项工作将\textbf{(1)}给出第一个联邦学习度量聚合方法\textbf{(2)}展示有效秩在联邦问题上表现优于基线联邦平均\cite{konevcny2016federated}和\textbf{(3)}开发一种依赖于有效秩的新权重聚合方案。 显示英文摘要

摘要： 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.