Support vector machine for distributed classification: A dynamic consensus approach

A totally distributed and scalable support vector machine (DSVM) for classification in ad hoc wireless sensor networks (WSNs) is proposed. A sequential gradient ascent based algorithm is first introduced and adapted for distributed and parallel SVM training using only the local dataset for each classification agent. Then the global nonlinear classifier is evaluated via a dynamic consensus algorithm with communication between neighbors instead of among all agents in the network. The proposed algorithm is totally distributed and parallel, thus the requirement of data privacy can be satisfied since it requires no information exchange among agents during training. What's more, since it only exchanges information between neighbors during evaluation, the proposed algorithm is scalable for large-scale sensor network and considerable communication energy can be reduced which will prolong the lifetime of the whole network.