A Low-Complexity Universal Scheme for Rate-Constrained Distributed Regression Using a Wireless Sensor Network

We propose a scheme for rate-constrained distributed nonparametric regression using a wireless sensor network. The scheme is universal across a wide range of sensor noise models, including unbounded and nonadditive noise; it has low complexity, requiring simple operations such as uniform scalar quantization with dither and message passing between neighboring nodes in the network, and attains minimax optimality for regression functions in common smoothness classes. We present theoretical results on the tradeoff between the compression rate, communication complexity of encoding, and the MSE and demonstrate empirical performance of the scheme using simulations.