On the Learning Behavior of Adaptive Networks—Part II: Performance Analysis


Half I of this paper examined the mean-sq. stability and convergence of the learning method of distributed methods over graphs. The results identified conditions on the network topology, utilities, and information in order to make sure stability; the results also identified 3 distinct stages in the educational behavior of multiagent networks related to transient phases I and II and therefore the steady-state section. This Half II examines the steady-state part of distributed learning by networked agents. Apart from characterizing the performance of the individual agents, it is shown that the network induces a useful equalization effect across all agents. In this approach, the performance of noisier agents is enhanced to the same level because the performance of agents with less noisy information. It's more shown that in the little step-size regime, every agent in the network is able to realize the identical performance level as that of a centralized strategy reminiscent of a totally connected network. The ends up in this half reveal explicitly that aspects of the network topology and operation influence performance and offer vital insights into the look of effective mechanisms for the processing and diffusion of knowledge over networks.

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