Sample Greedy Gossip for Distributed Network-Wide Average Computation

TL;DR

This paper introduces a new greedy gossip algorithm for distributed averaging that balances communication cost and convergence speed through stochastic sampling, supported by theoretical analysis and simulations.

Contribution

It proposes a novel greedy gossip algorithm using stochastic sampling for flexible trade-offs in distributed averaging, with theoretical convergence analysis.

Findings

The algorithm converges efficiently with reduced communication costs.

Theoretical analysis confirms the convergence properties.

Simulations validate the balance between communication and convergence speed.

Abstract

This paper investigates the problem of distributed network-wide averaging and proposes a new greedy gossip algorithm. Instead of finding the optimal path of each node in a greedy manner, the proposed approach utilises a suboptimal communication path by performing greedy selection among randomly selected active local nodes. Theoretical analysis on convergence speed is also performed to investigate the characteristics of the proposed algorithm. The main feature of the new algorithm is that it provides great flexibility and well balance between communication cost and convergence performance introduced by the stochastic sampling strategy. Extensive numerical simulations are performed to validate the analytic findings.