Two-way source coding with a fidelity criterion
IEEE Transactions on Information Theory
Peer-to-Peer Membership Management for Gossip-Based Protocols
IEEE Transactions on Computers
Gossip-Based Computation of Aggregate Information
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Locally constructed algorithms for distributed computations in ad-hoc networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Geographic gossip: efficient aggregation for sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Distributed average consensus with least-mean-square deviation
Journal of Parallel and Distributed Computing
Automatica (Journal of IFAC)
Distributed Average Consensus using Probabilistic Quantization
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Gossip consensus algorithms via quantized communication
Automatica (Journal of IFAC)
Cascade multiterminal source coding
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 2
Coding With Side Information for Rate-Constrained Consensus
IEEE Transactions on Signal Processing - Part I
IEEE Transactions on Information Theory
| Hi-index | 754.84 |
In this paper, an information theoretic formulation of the distributed averaging problem previously studied in computer science and control is presented. We assume a network with m nodes each observing a white Gaussian noise (WGN) source. The nodes communicate and perform local processing with the goal of computing the average of the sources to within a prescribed mean squared error distortion. The network rate distortion function R*(D) for a two-node network with correlated Gaussian sources is established. A general cutset lower bound on R*(D) is established and shown to be achievable to within a factor of 2 via a centralized protocol over a star network. A lower bound on the network rate distortion function for distributed weighted-sum protocols, which is larger in order than the cutset bound by a factor of log m, is established. An upper bound on the network rate distortion function for gossip-base weighted-sum protocols, which is only log log m larger in order than the lower bound for a complete graph network, is established. The results suggest that using distributed protocols results in a factor of log m increase in order relative to centralized protocols.