Research on the Distributed Training Method for Linear SVM in WSN

In Wireless Sensor Network (WSN), transferring all training samples distributed across different nodes to a centralized fusion center for training Support Vector Machine (SVM) significantly increases the communication overhead and energy consumption. Therefore, this paper studies the distributed training approach for linear SVM through the collaboration of neighboring nodes within the networks. First, the centralized linear SVM problem is cast as the solution of coupled decentralized convex optimization sub-problems with consensus constraints on the classifier parameters. Second, the distributed linear SVM problem is solved and derived using the augmented Lagrange multipliers method, and a novel distributed training algorithm, called Average Consensus based Distributed Supported Vector Machine (AC-DSVM), is proposed. To decrease the communication overhead of global average consensus, an improved distributed training algorithm, named Once Average Consensus based Distributed Supported Vector Machine (1-AC-DSVM), is presented, which is only based on once global average consensus. Simulation results show that compared with existing algorithms, AC-DSVM has slightly higher iterations and data traffic, but can converge to the centralized training results; 1-AC-DSVM not only has better convergence, but also has remarkable advantage in convergence speed and data traffic.