Differentially-Private Constrained Consensus and Distributed Optimization with Guaranteed Convergence
The authors propose the first distributed constrained optimization algorithm that can ensure both provable convergence to a global optimal solution and rigorous ε-differential privacy, even when the number of iterations tends to infinity.
