Distributed consensus algorithms for SVM training in wireless sensor networks

This paper studies coordination and consensus mechanisms for Wireless sensor networks in order to train a Support Vector Machine (SVM) classifier in a distributed fashion. We propose two selective gossip algorithms, which take advantage of the sparse representation that SVMs provide for their decision boundary (hyperplane), in order to ensure convergence to an optimal or close-to-optimal classifier, while minimizing the required amount of information exchange between neighboring sensors. The first proposed algorithm calls for the local exchange of support vectors between sensors, while the second technique requires the exchange of all sample vectors that define uniquely and completely the convex hulls of the two classes. Through simulation experiments, we show that the proposed algorithms achieve a consensus close to the desired hyperplane obtained with a centralized SVM-based classifier that uses the entire sensor data.