Optimal distributed composite testing in high-dimensional Gaussian models with 1-bit communication
This work addresses the problem of efficient signal detection under severe communication constraints for distributed systems, which is relevant for researchers working on distributed inference and communication-efficient algorithms.
This paper investigates signal detection in high-dimensional Gaussian models with 1-bit communication in a distributed setting. The authors establish a lower bound on the Euclidean norm required for signal detectability and propose optimal distributed testing strategies that achieve this bound.
In this paper we study the problem of signal detection in Gaussian noise in a distributed setting where the local machines in the star topology can communicate a single bit of information. We derive a lower bound on the Euclidian norm that the signal needs to have in order to be detectable. Moreover, we exhibit optimal distributed testing strategies that attain the lower bound.