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Distributed Algorithms for Average Consensus of Input Data With Fast Convergence
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DOI：10.1109/TSMC.2019.2914385.png)
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Abstract
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This paper proposes fast convergent distributed algorithms for weighted average consensus of input data. For acyclic graphs, we give an algorithm that converges to the exact weighted average consensus in a finite number of iterations, equal to the graph diameter. For loopy (cyclic) graphs, we offer two remedies. In the first one, we give another distributed algorithm to enable our average consensus algorithm applicable to a loopy graph by converting it into a spanning tree. In the second one, we consider a slightly modified average consensus problem whose optimal solution approximates the consensus solution with arbitrary precision, and give a modified average consensus algorithm with guaranteed exponential convergence to the optimal solution. The proposed average consensus algorithms enjoy low complexities, robustness to transmission adversaries, and asynchronous implementation. Our algorithms are conceptually different from the popular graph Laplacian approach, and converge much faster than the latter approach.
Keywords:
Distributed algorithms
Convergence
Consensus algorithm
Nickel
Distributed databases
Complexity theory
Approximation algorithms
Average consensus
distributed algorithms
distributed estimation
networked control
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