Distributed Nonparametric Function Estimation: Optimal Rate of Convergence and Cost of Adaptation
This work addresses the problem of efficient function estimation in distributed systems for statisticians and machine learning practitioners, providing foundational insights into adaptation costs.
The paper tackled distributed nonparametric function estimation under communication constraints, establishing the minimax rate of convergence and quantifying the exact communication cost for adaptation, showing that adaptation cannot be achieved for free in distributed settings.
Distributed minimax estimation and distributed adaptive estimation under communication constraints for Gaussian sequence model and white noise model are studied. The minimax rate of convergence for distributed estimation over a given Besov class, which serves as a benchmark for the cost of adaptation, is established. We then quantify the exact communication cost for adaptation and construct an optimally adaptive procedure for distributed estimation over a range of Besov classes. The results demonstrate significant differences between nonparametric function estimation in the distributed setting and the conventional centralized setting. For global estimation, adaptation in general cannot be achieved for free in the distributed setting. The new technical tools to obtain the exact characterization for the cost of adaptation can be of independent interest.