A Communication-Efficient Distributed Optimization Algorithm for Problems with Coupling Constraints

Resource allocation is a fundamental problem in Industrial Internet of Things (IIoT) systems, in which devices work together under limited communication bandwidth to complete diverse tasks. This paper proposes a communication-efficient distributed optimization algorithm tailored for problems with coupled constraints. To tackle coupled constraints, we solve the problem via its dual counterpart, and develop a compressed version. Difference compression and dynamic scaling factors are then introduced to mitigate compression errors. We show that the proposed algorithm converges linearly for strongly convex and smooth objective functions. Numerical simulations validate the theoretical results and demonstrate the efficiency and robustness of the proposed algorithm.