  In this paper, we consider the binary classification problem via distributed
Support-Vector-Machines (SVM), where the idea is to train a network of agents,
with limited share of data, to cooperatively learn the SVM classifier for the
global database. Agents only share processed information regarding the
classifier parameters and the gradient of the local loss functions instead of
their raw data. In contrast to the existing work, we propose a continuous-time
algorithm that incorporates network topology changes in discrete jumps. This
hybrid nature allows us to remove chattering that arises because of the
discretization of the underlying CT process. We show that the proposed
algorithm converges to the SVM classifier over time-varying weight balanced
directed graphs by using arguments from the matrix perturbation theory.
