  In the era of big data, an important weapon in a machine learning
researcher's arsenal is a scalable Support Vector Machine (SVM) algorithm. SVMs
are extensively used for solving classification problems. Traditional
algorithms for learning SVMs often scale super linearly with training set size
which becomes infeasible very quickly for large data sets. In recent years,
scalable algorithms have been designed which study the primal or dual
formulations of the problem. This often suggests a way to decompose the problem
and facilitate development of distributed algorithms. In this paper, we present
a distributed algorithm for learning linear Support Vector Machines in the
primal form for binary classification called Gossip-bAseD sub-GradiEnT (GADGET)
SVM. The algorithm is designed such that it can be executed locally on nodes of
a distributed system. Each node processes its local homogeneously partitioned
data and learns a primal SVM model. It then gossips with random neighbors about
the classifier learnt and uses this information to update the model. Extensive
theoretical and empirical results suggest that this anytime algorithm has
performance comparable to its centralized and online counterparts.
