Distributed and Rate-Adaptive Feature Compression

arXiv:2404.02179v1 Announce Type: cross
Abstract: We study the problem of distributed and rate-adaptive feature compression for linear regression. A set of distributed sensors collect disjoint features of regressor data. A fusion center is assumed to contain a pretrained linear regression model, trained on a dataset of the entire uncompressed data. At inference time, the sensors compress their observations and send them to the fusion center through communication-constrained channels, whose rates can change with time. Our goal is to design a …

abstract arxiv center compression cs.ai cs.it data dataset distributed feature features fusion inference linear linear regression math.it rate regression sensors set stat.ml study type