  In this paper, we investigate unconstrained and constrained sample-based
federated optimization, respectively. For each problem, we propose a privacy
preserving algorithm using stochastic successive convex approximation (SSCA)
techniques, and show that it can converge to a Karush-Kuhn-Tucker (KKT) point.
To the best of our knowledge, SSCA has not been used for solving federated
optimization, and federated optimization with nonconvex constraints has not
been investigated. Next, we customize the two proposed SSCA-based algorithms to
two application examples, and provide closed-form solutions for the respective
approximate convex problems at each iteration of SSCA. Finally, numerical
experiments demonstrate inherent advantages of the proposed algorithms in terms
of convergence speed, communication cost and model specification.
