Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
Incremental Subgradient Methods for Nondifferentiable Optimization
SIAM Journal on Optimization
Decentralized compression and predistribution via randomized gossiping
Proceedings of the 5th international conference 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
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Automatica (Journal of IFAC)
The speed of greed: Characterizing myopic gossip through network voracity
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Incremental Stochastic Subgradient Algorithms for Convex Optimization
SIAM Journal on Optimization
The capacity of wireless networks
IEEE Transactions on Information Theory
Distributed function calculation and consensus using linear iterative strategies
IEEE Journal on Selected Areas in Communications
Reaching consensus in wireless networks with probabilistic broadcast
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
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This paper presents greedy gossip with eavesdropping (GGE), a novel randomized gossip algorithm for distributed computation of the average consensus problem. In gossip algorithms, nodes in the network randomly communicate with their neighbors and exchange information iteratively. The algorithms are simple and decentralized, making them attractive for wireless network applications. In general, gossip algorithms are robust to unreliable wireless conditions and time varying network topologies. In this paper, we introduce GGE and demonstrate that greedy updates lead to rapid convergence. We do not require nodes to have any location information. Instead, greedy updates are made possible by exploiting the broadcast nature of wireless communications. During the operation of GGE, when a node decides to gossip, instead of choosing one of its neighbors at random, it makes a greedy selection, choosing the node which has the value most different from its own. In order to make this selection, nodes need to know their neighbors' values. Therefore, we assume that all transmissions are wireless broadcasts and nodes keep track of their neighbors' values by eavesdropping on their communications. We show that the convergence of GGE is guaranteed for connected network topologies. We also study the rates of convergence and illustrate, through theoretical bounds and numerical simulations, that GGE consistently outperforms randomized gossip and performs comparably to geographic gossip on moderate-sized random geometric graph topologies.