  We study the risk performance of distributed learning for the regularization
empirical risk minimization with fast convergence rate, substantially improving
the error analysis of the existing divide-and-conquer based distributed
learning. An interesting theoretical finding is that the larger the diversity
of each local estimate is, the tighter the risk bound is. This theoretical
analysis motivates us to devise an effective maxdiversity distributed learning
algorithm (MDD). Experimental results show that MDD can outperform the existing
divide-andconquer methods but with a bit more time. Theoretical analysis and
empirical results demonstrate that our proposed MDD is sound and effective.
