Abstract: A parallel learning algorithm for solving large scale kernel logistic regression problems is presented. Primal optimization problem for the kernel logistic regression is switched to the dual problem by using Fenchel duality theory in convex optimization. Then, learning the classifiers on subsets of training data can run independently when block-update methods are employed. A simple customer-server parallel computing mode is designed that each customer node learns a sub-problem for the subset of training data. Server node receives the messages passed by all customer nodes after one optimization iteration is end, followed by updating the objective functions of sub-problems. In comparison to non-parallel learning algorithms on standardized datasets, we obtain encouraging results.