Subspace Recovery from Heterogeneous Data with Non-isotropic Noise

*= Equal Contributions
Recovering linear subspaces from data is a fundamental and important task in statistics and machine learning. Motivated by heterogeneity in Federated Learning settings, we study a basic formulation of this problem: the principal component analysis (PCA), with a focus on dealing with irregular noise. Our data come from users with user contributing data samples from a -dimensional distribution with mean . Our goal is to recover the linear subspace shared by using the data points from all users, where every data point from user is formed by adding an independent…Apple Machine Learning Research