  Conformal Prediction is a machine learning methodology that produces valid
prediction regions under mild conditions. In this paper, we explore the
application of making predictions over multiple data sources of different sizes
without disclosing data between the sources. We propose that each data source
applies a transductive conformal predictor independently using the local data,
and that the individual predictions are then aggregated to form a combined
prediction region. We demonstrate the method on several data sets, and show
that the proposed method produces conservatively valid predictions and reduces
the variance in the aggregated predictions. We also study the effect that the
number of data sources and size of each source has on aggregated predictions,
as compared with equally sized sources and pooled data.
