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

GADGET SVM: A Gossip-bAseD sub-GradiEnT Solver for Linear SVMs