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.　©,　2015,　Science　Press.　All　right　reserved.