On distributed online classification in the midst of concept drifts
In this work, we analyze the generalization ability of distributed online learning algorithms under stationary and non-stationary environments. We derive bounds for the excess-risk attained by each node in a connected network of learners and study the performance advantage that diffusion has over individual non-cooperative processing. We conduct extensive simulations to illustrate the results.
classification distributed stochastic optimization diffusion adaptation non-stationary data tracking risk function loss function excess risk