Seeking Consensus on Subspaces in Federated Principal Component Analysis. (arXiv:2012.03461v4 [math.OC] UPDATED)

In this paper, we develop an algorithm for federated principal component
analysis (PCA) with emphases on both communication efficiency and data privacy.
Generally speaking, federated PCA algorithms based on direct adaptations of
classic iterative methods, such as simultaneous subspace iterations (SSI), are
unable to preserve data privacy, while algorithms based on variable-splitting
and consensus-seeking, such as alternating direction methods of multipliers
(ADMM), lack in communication-efficiency. In this work, we propose a novel
consensus-seeking formulation by equalizing subspaces spanned by splitting …

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