When distributed data sources have different contexts the problem of Distributed Re-gression becomes severe. It is the underlying law of probability that constitutes the context of a source. A new Distributed Regression System is presented, which makes use of a discrete rep-resentation of the probability density functions (pdfs). Neighborhoods of similar datasets are detected by comparing their approximated pdfs. This information supports an ensemble-basedapproach, and the improvement of a second level unit, as it is the case in stacked generalization. Two synthetic and six real data sets are used to compare the proposed method with otherstate-of-the-art models. The obtained results are positive for most datasets.

distributed machine learning, context-aware regression, similarity representation