P2P RVM for Distributed Classification

In recent years there is an increasing interest for analytical methods that learn patterns over large-scale data distributed over Peer-to-Peer (P2P) networks and support applications. Mining patterns in such distributed and dynamic environment is a challenging task, because centralization of data is not feasible. In this paper, we have proposed a distributed classification technique based on relevance vector machines (RVM) and local model exchange among neighboring peers in a P2P network. In such networks, the evaluation criteria for an efficient distributed classification algorithm is based on the size of resulting local models (communication efficiency) and their prediction accuracy. RVM utilizes dramatically fewer kernel functions than a state-of-the-art "support vector machine" (SVM), while demonstrating comparable generalization performance. This makes RVM a suitable choice to learn compact and accurate local models at each peer in a P2P network. Our model propagation approach, exchange resulting models with peers in a local neighborhood to produce more accurate network wide global model, while keeping the communication cost low throughout the network. Through extensive experimental evaluations, we demonstrate that by using more relevant and compact models, our approach outperforms the baseline model propagation approaches in terms of accuracy and communication cost.