Cloud-Based Centralized/Decentralized Multi-Agent Optimization with Communication Delays

TL;DR

This paper introduces a hybrid multi-agent optimization framework combining decentralized agent communication with centralized cloud computation, explicitly accounting for communication delays, and proves its convergence.

Contribution

It proposes a primal-dual algorithm within a hybrid architecture that handles communication delays and provides convergence guarantees for convex optimization problems.

Findings

Algorithm converges under communication delays

Hybrid architecture improves scalability and robustness

Experimental results validate theoretical analysis

Abstract

We present and analyze a computational hybrid architecture for performing multi-agent optimization. The optimization problems under consideration have convex objective and constraint functions with mild smoothness conditions imposed on them. For such problems, we provide a primal-dual algorithm implemented in the hybrid architecture, which consists of a decentralized network of agents into which centralized information is occasionally injected, and we establish its convergence properties. To accomplish this, a central cloud computer aggregates global information, carries out computations of the dual variables based on this information, and then distributes the updated dual variables to the agents. The agents update their (primal) state variables and also communicate among themselves with each agent sharing and receiving state information with some number of its neighbors. Throughout, communications with the cloud are not assumed to be synchronous or instantaneous, and communication delays are explicitly accounted for in the modeling and analysis of the system. Experimental results are presented to support the theoretical developments made.