  In the supervised learning domain, considering the recent prevalence of
algorithms with high computational cost, the attention is steering towards
simpler, lighter, and less computationally extensive training and inference
approaches. In particular, randomized algorithms are currently having a
resurgence, given their generalized elementary approach. By using randomized
neural networks, we study distributed classification, which can be employed in
situations were data cannot be stored at a central location nor shared. We
propose a more efficient solution for distributed classification by making use
of a lossy compression approach applied when sharing the local classifiers with
other agents. This approach originates from the framework of hyperdimensional
computing, and is adapted herein. The results of experiments on a collection of
datasets demonstrate that the proposed approach has usually higher accuracy
than local classifiers and getting close to the benchmark - the centralized
classifier. This work can be considered as the first step towards analyzing the
variegated horizon of distributed randomized neural networks.
