  In the Internet-of-Things (IoT) systems, there are plenty of informative data
provided by a massive number of IoT devices (e.g., sensors). Learning a
function from such data is of great interest in machine learning tasks for IoT
systems. Focusing on streaming (or sequential) data, we present a
privacy-preserving distributed online learning framework with multiplekernels
(named DOMKL). The proposed DOMKL is devised by leveraging the principles of an
online alternating direction of multipliers (OADMM) and a distributed Hedge
algorithm. We theoretically prove that DOMKL over T time slots can achieve an
optimal sublinear regret, implying that every learned function achieves the
performance of the best function in hindsight as in the state-of-the-art
centralized online learning method. Moreover, it is ensured that the learned
functions of any two neighboring learners have a negligible difference as T
grows, i.e., the so-called consensus constraints hold. Via experimental tests
with various real datasets, we verify the effectiveness of the proposed DOMKL
on regression and time-series prediction tasks.
