Distributed Boosting Algorithm Over Multi-agent Networks

This paper investigates a distributed design for boosting methods, especially AdaBoost, over multi-agent networks. In fact, we present a distributed AdaBoost algorithm for solving a distributed classification problem in machine learning through sharing classifiers among agents. Our algorithm can effectively avoid overfitting, efficiently merge the feature information of other agents, and moreover, largely reduce the communication cost in comparison with some existing centralized or distributed algorithms. Furthermore, simulations with a real classification dataset is given to show the effectiveness of the proposed algorithm. The performance of the proposed algorithm matches that of the centralized AdaBoost algorithm.