Distributed anonymous function computation in information fusion and multiagent systems
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Convergence speed of binary interval consensus
INFOCOM'10 Proceedings of the 29th conference on Information communications
Distributed machine learning in networks by consensus
Neurocomputing
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We design distributed and quantized average consensus algorithms on arbitrary connected networks. By construction, quantized algorithms cannot produce a real, analog average. Instead, our algorithm reaches consensus on the quantized interval that contains the average. We prove that this consensus in reached in finite time almost surely. As a byproduct of this convergence result, we show that the majority voting problem is solvable with only 2 bits of memory per agent.