Distributed Sketching Methods for Privacy Preserving Regression

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In this work, we study distributed sketching methods for large scale regression problems.

• We leverage multiple randomized sketches for reducing the problem dimensions as well as preserving privacy and improving straggler resilience in asynchronous distributed systems.
• We derive novel approximation guarantees for classical sketching methods and analyze the accuracy of parameter averaging for distributed sketches.
• We consider random matrices including Gaussian, randomized Hadamard, uniform sampling and leverage score sampling in the distributed setting.