This paper focuses on the problem of distributed sequence
prediction in a network of sparsely interconnected agents,
where agents collaborate to achieve provably reasonable
predictive performance. An expert assisted online learning
algorithm in a distributed setup of the consensus+innovations
form is proposed, in which the agents update their weights
for the experts’ predictions by simultaneously processing the
latest network losses (innovations) and the cumulative losses
obtained from neighboring agents (consensus). This paper
characterizes the regret of the agents’ prediction in lieu of
the proposed distributed online learning algorithm and establishes
the sub-linear regret of the agents’ predictions with
respect to the best forecasting expert.