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A new challenge for learning algorithms in cyber-physical network systems isthe distributed solution of big-data classification problems, i.e., problems inwhich both the number of training samples and their dimension is high.Motivated by several problem set-ups in Machine Learning, in this paper weconsider a special class of quadratic optimization problems involving a "large"number of input data, whose dimension is "big". To solve these quadraticoptimization problems over peer-to-peer networks, we propose an asynchronous,distributed algorithm that scales with both the number and the dimension of theinput data (training samples in the classification problem). The proposeddistributed optimization algorithm relies on the notion of "core-set" which isused in geometric optimization to approximate the value function associated toa given set of points with a smaller subset of points. By computing localcore-sets on a smaller version of the global problem and exchanging them withneighbors, the nodes reach consensus on a set of active constraintsrepresenting an approximate solution for the global quadratic program.

A core-set approach for distributed quadratic programming in big-data classification