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.

翻译：当前在联邦学习环境下，尚无任何学习方案利用可解释人工智能（XAI）这一新兴研究领域，尤其是那些有助于衡量模型学习效果的新型学习度量。其中一种新型学习度量被称为"有效秩"（ER），它通过计算矩阵奇异值的香农熵，提供了一种衡量网络层映射效果的指标。通过将联邦学习与有效秩这一学习度量相结合，本研究将：（1）首次提出基于学习度量的联邦聚合方法；（2）通过超越基准方法联邦平均算法，证明有效秩适用于联邦问题，并（3）开发一种基于有效秩的新型权重聚合方案。