Using Hierarchical Decomposition to Speed up Average Consensus

We study the continuous-time consensus problem where nodes on a graph attempt to reach average consensus. We consider communication graphs that can be decomposed into a hierarchical structure and present a consensus scheme that exploits this hierarchical topology. The scheme consists of splitting the overall graph into layers of smaller connected subgraphs. Consensus is performed within the individual subgraphs starting with those of the lowest layer of the hierarchy and moving upwards. Certain “leader” nodes bridge the layers of the hierarchy. By exploiting the increased convergence speed of the smaller subgraphs, we show how this scheme can achieve faster overall convergence than the standard single-stage consensus algorithm running on the full graph topology. The result presents some fundamentals on how the communication architecture influences the global performance of a networked system. Analytical performance bounds are derived and simulations provided to illustrate the effectiveness of the scheme.