  Distributed, online data mining systems have emerged as a result of
applications requiring analysis of large amounts of correlated and
high-dimensional data produced by multiple distributed data sources. We propose
a distributed online data classification framework where data is gathered by
distributed data sources and processed by a heterogeneous set of distributed
learners which learn online, at run-time, how to classify the different data
streams either by using their locally available classification functions or by
helping each other by classifying each other's data. Importantly, since the
data is gathered at different locations, sending the data to another learner to
process incurs additional costs such as delays, and hence this will be only
beneficial if the benefits obtained from a better classification will exceed
the costs. We assume that the classification functions available to each
processing element are fixed, but their prediction accuracy for various types
of incoming data are unknown and can change dynamically over time, and thus
they need to be learned online. We model the problem of joint classification by
the distributed and heterogeneous learners from multiple data sources as a
distributed contextual bandit problem where each data is characterized by a
specific context. We develop distributed online learning algorithms for which
we can prove that they have sublinear regret. Compared to prior work in
distributed online data mining, our work is the first to provide analytic
regret results characterizing the performance of the proposed algorithms.
