Energy distance with a Taylor approximation is proposed as a scalable feature-heterogeneity measure for federated learning, but the core approximation is underived, its multivariate form is inaccurate, and the proposed penalty-weighting benefit is untested.
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Measuring Heterogeneity in Machine Learning with Distributed Energy Distance
Energy distance with a Taylor approximation is proposed as a scalable feature-heterogeneity measure for federated learning, but the core approximation is underived, its multivariate form is inaccurate, and the proposed penalty-weighting benefit is untested.