Distributed training of multiclass conic-segmentation support vector machines on communication constrained networks

We present a distributed algorithm for training multiclass conic-segmentation support vector machines (CS-SVMs) on communication-constrained networks. The proposed algorithm takes advantage of the sparsity of the CS-SVM to minimise the communication overhead between nodes during training to obtain classifiers at each node which closely approximate the optimal (centralised) classifier. The proposed algorithm is also suited for wireless sensor networks where inter-node communication is limited by power restrictions and bandwidth. We demonstrate our algorithm by applying it to two datasets, one simulated and one benchmark dataset, to show that the global decision functions found by the nodes closely approximate the optimal decision function found by a centralised algorithm possessing all training data in one batch.