Critical Connectivity and Fastest Convergence Rates of Distributed Consensus with Switching Topologies and Additive Noises

TL;DR

This paper investigates the critical conditions and optimal convergence rates for distributed consensus algorithms in networks with switching topologies and additive noise, introducing new connectivity conditions and practical control strategies.

Contribution

It introduces the extensible joint-connectivity condition with a parameter $oldsymbol{ extit{ extbf{ extdelta}}}$, establishing the critical $oldsymbol{ extit{ extdelta}=1/2}$ for consensus and analyzing optimal convergence rates.

Findings

Critical $ extit{ extdelta}=1/2$ for consensus under extensible joint-connectivity.

Fastest convergence rate of order 1/t for ideal topologies.

Practical open-loop controls achieve the optimal convergence rate.

Abstract

Consensus conditions and convergence speeds are crucial for distributed consensus algorithms of networked systems. Based on a basic first-order average-consensus protocol with time-varying topologies and additive noises, this paper first investigates its critical consensus condition on network topology by stochastic approximation frameworks. A new joint-connectivity condition called extensible joint-connectivity that contains a parameter $\delta$ (termed the extensible exponent) is proposed. With this and a balanced topology condition, we show that a critical value of $\delta$ for consensus is $1/2$. Optimization on convergence rate of this protocol is further investigated. It is proved that the fastest convergence rate, which is the theoretic optimal rate among all controls, is of the order $1/t$ for the best topologies, and is of the order $1/t^{1-2\delta}$ for the worst topologies which are balanced and satisfy the extensible joint-connectivity condition. For practical implementation, certain open-loop control strategies are introduced to achieve consensus with a convergence rate of the same order as the fastest convergence rate. Furthermore, a consensus condition is derived for non stationary and strongly correlated random topologies. The algorithms and consensus conditions are applied to distributed consensus computation of mobile ad-hoc networks; and their related critical exponents are derived from relative velocities of mobile agents for guaranteeing consensus.