FedPower: Privacy-Preserving Distributed Eigenspace Estimation

Understand

Eigenspace estimation is fundamental in machine learning and statistics, which has found applications in PCA, dimension reduction, and clustering, among others.

• The modern machine learning community usually assumes that data come from and belong to different organizations.
• The low communication power and the possible privacy breaches of data make the computation of eigenspace challenging.
• To address these challenges, we propose a class of algorithms called \textsf{FedPower} within the federated learning (FL) framework.