Probabilistic consensus via polling and majority rules

In this paper, we consider lightweight decentralised algorithms for achieving consensus in distributed systems. Each member of a distributed group has a private value from a fixed set consisting of, say, two elements, and the goal is for all members to reach consensus on the majority value. We explore variants of the voter model applied to this problem. In the voter model, each node polls a randomly chosen group member and adopts its value. The process is repeated until consensus is reached. We generalise this so that each member polls a (deterministic or random) number of other group members and changes opinion only if a suitably defined super-majority has a different opinion. We show that this modification greatly speeds up the convergence of the algorithm, as well as substantially reducing the probability of it reaching consensus on the incorrect value.

[1]  P. Donnelly,et al.  Finite particle systems and infection models , 1983, Mathematical Proceedings of the Cambridge Philosophical Society.

[2]  N. Biggs RANDOM WALKS AND ELECTRICAL NETWORKS (Carus Mathematical Monographs 22) , 1987 .

[3]  David Peleg,et al.  Distributed Probabilistic Polling and Applications to Proportionate Agreement , 1999, ICALP.

[4]  James Surowiecki The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies, and nations Doubleday Books. , 2004 .

[5]  R. Srikant,et al.  Quantized Consensus , 2006, 2006 IEEE International Symposium on Information Theory.

[6]  Devavrat Shah,et al.  Fast Distributed Algorithms for Computing Separable Functions , 2005, IEEE Transactions on Information Theory.

[7]  John N. Tsitsiklis,et al.  Convergence Speed in Distributed Consensus and Averaging , 2009, SIAM J. Control. Optim..

[8]  Milan Vojnovic,et al.  Using Three States for Binary Consensus on Complete Graphs , 2009, IEEE INFOCOM 2009.

[9]  Martin Vetterli,et al.  Interval consensus: From quantized gossip to voting , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[10]  Moez Draief,et al.  Convergence Speed of Binary Interval Consensus , 2010, 2010 Proceedings IEEE INFOCOM.

[11]  J. Tsitsiklis,et al.  Distributed anonymous discrete function computation and averaging , 2011 .

[12]  A. Montanari,et al.  Majority dynamics on trees and the dynamic cavity method , 2009, 0907.0449.

[13]  John N. Tsitsiklis,et al.  Distributed Anonymous Discrete Function Computation , 2010, IEEE Transactions on Automatic Control.

[14]  Moez Draief,et al.  Consensus on the Initial Global Majority by Local Majority Polling for a Class of Sparse Graphs , 2012, 1209.5025.

[15]  Elchanan Mossel,et al.  Majority dynamics and aggregation of information in social networks , 2012, Autonomous Agents and Multi-Agent Systems.

[16]  Sanjeev R. Kulkarni,et al.  An upper bound on the convergence time for quantized consensus , 2013, INFOCOM.

[17]  Colin Cooper,et al.  Coalescing Random Walks and Voting on Connected Graphs , 2012, SIAM J. Discret. Math..

[18]  Mohammed Abdullah,et al.  Global majority consensus by local majority polling on graphs , 2012, 2014 7th International Conference on NETwork Games, COntrol and OPtimization (NetGCoop).