Lower bound performances for average consensus in open multi-agent systems
(2019) Conference on Decision and Control (CDC 2019) — Location: Nice (France) (11.December.2019)
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- Abstract
- We derive fundamental limitations on the performances of intrinsic averaging algorithms in open multi-agent systems, which are systems subject to random arrivals and departures of agents. Each agent holds a value, and their goal is to estimate the average of the values of the agents presently in the system. We provide a lower bound on the expected Mean Square Error for any estimation algorithm, assuming that the number of agents remains constant and that communications are random and pairwise. Our derivation is based on the expected error obtained with an optimal algorithm under conditions more favorable than those the actual problem allows, and relies on an analysis of the constraints on the information spreading mechanisms in the system, and relaxations of these.
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Citations
Monnoyer de Galland de Carnières, C., & Hendrickx, J. (2019). Lower bound performances for average consensus in open multi-agent systems. Proceedings of the 56th IEEE Conference on Decision and Control, 7429-7434. https://doi.org/10.1109/CDC40024.2019.9029319 (Original work published 2019)