  We present a distributed solution to optimizing a convex function composed of
several non-convex functions. Each non-convex function is privately stored with
an agent while the agents communicate with neighbors to form a network. We show
that coupled consensus and projected gradient descent algorithm proposed in [1]
can optimize convex sum of non-convex functions under an additional assumption
on gradient Lipschitzness. We further discuss the applications of this analysis
in improving privacy in distributed optimization.
