Asynchronous Adaptation and Learning over Networks - Part II: Performance Analysis
This provides theoretical guarantees for asynchronous adaptation in networks, which is incremental but important for robust distributed systems.
The paper analyzes the performance of asynchronous distributed optimization over networks, deriving analytical expressions for mean-square convergence rate and steady-state deviation, showing agents achieve near-agreement with O(ν^(1+γ_o')) and solution accuracy within O(ν).
In Part I \cite{Zhao13TSPasync1}, we introduced a fairly general model for asynchronous events over adaptive networks including random topologies, random link failures, random data arrival times, and agents turning on and off randomly. We performed a stability analysis and established the notable fact that the network is still able to converge in the mean-square-error sense to the desired solution. Once stable behavior is guaranteed, it becomes important to evaluate how fast the iterates converge and how close they get to the optimal solution. This is a demanding task due to the various asynchronous events and due to the fact that agents influence each other. In this Part II, we carry out a detailed analysis of the mean-square-error performance of asynchronous strategies for solving distributed optimization and adaptation problems over networks. We derive analytical expressions for the mean-square convergence rate and the steady-state mean-square-deviation. The expressions reveal how the various parameters of the asynchronous behavior influence network performance. In the process, we establish the interesting conclusion that even under the influence of asynchronous events, all agents in the adaptive network can still reach an $O(ν^{1 + γ_o'})$ near-agreement with some $γ_o' > 0$ while approaching the desired solution within $O(ν)$ accuracy, where $ν$ is proportional to the small step-size parameter for adaptation.