Interaction Is Necessary for Distributed Learning with Privacy or Communication Constraints
Explore a conference talk that delves into the necessity of interaction for distributed learning under privacy or communication constraints. Examine the differences between non-interactive and interactive settings in stochastic convex optimization with local privacy. Learn about prior work on private stochastic convex optimization, including upper bounds and exponential sample complexity. Discover the lower bounds for non-interactive private linear classification and the statistical query model. Investigate the proof techniques used to demonstrate that learning with non-interactive local privacy requires exponential sample complexity. Gain insights into two non-distinguishable distributions over {-1,1}d and consider open questions in the field of distributed learning with privacy constraints.