  Consider a star network where each local node possesses a set of test
statistics that exhibit a symmetric distribution around zero when their
corresponding null hypothesis is true. This paper investigates statistical
inference problems in networks concerning the aggregation of this general type
of statistics and global error rate control under communication constraints in
various scenarios. The study proposes communication-efficient algorithms that
are built on established non-parametric methods, such as the Wilcoxon and sign
tests, as well as modern inference methods such as the Benjamini-Hochberg (BH)
and Barber-Candes (BC) procedures, coupled with sampling and quantization
operations. The proposed methods are evaluated through extensive simulation
studies.
