Satrap: Data and Network Heterogeneity Aware P2P Data-Mining

Distributed classification aims to build an accurate classifier by learning from distributed data while reducing computation and communication cost. A P2P network where numerous users come together to share resources like data content, bandwidth, storage space and CPU resources is an excellent platform for distributed classification. However, two important aspects of the learning environment have often been overlooked by other works, viz., 1) location of the peers which results in variable communication cost and 2) heterogeneity of the peers’ data which can help reduce redundant communication. In this paper, we examine the properties of network and data heterogeneity and propose a simple yet efficient P2P classification approach that minimizes expensive interregion communication while achieving good generalization performance. Experimental results demonstrate the feasibility and effectiveness of the proposed solution. keywords: Distributed classification, P2P network, cascade SVM.