Combinatorica
A proof of alon's second eigenvalue conjecture
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Gossip-Based Computation of Aggregate Information
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Geographic gossip: efficient aggregation for sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Asynchronous distributed averaging on communication networks
IEEE/ACM Transactions on Networking (TON)
Accelerated distributed average consensus via localized node state prediction
IEEE Transactions on Signal Processing
Convergence Speed in Distributed Consensus and Averaging
SIAM Journal on Control and Optimization
Optimization and analysis of distributed averaging with short node memory
IEEE Transactions on Signal Processing
Order-optimal consensus through randomized path averaging
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
| Hi-index | 22.14 |
Gossiping is a distributed process whose purpose is to enable the members of a group of n1 autonomous agents to asymptotically determine in a decentralized manner, the average of the initial values of their scalar gossip variables. This paper analyzes the accelerated gossip algorithms, first proposed in Cao, Spielman, and Yeh (2006), in which local memory is exploited by installing shift-registers at each agent. For the two-register case, the existence of the desired convergence is established under a symmetry assumption by separately studying the convergence in expectation and in mean square. In particular, the optimal rate of convergence in expectation is derived which is faster than that of the standard gossip algorithm, and a sufficient condition on the adjustable parameter for the convergence in mean square is provided. These theoretical results are validated for some classes of networks by comparison with existing empirical data. More general multi-register cases are also discussed.