Abstract
We are interested in the problem of computing the average consensus in a distributed fashion on random geometric graphs. We describe a new algorithm called Multi-scale Gossip which employs a hierarchical decomposition of the graph to partition the computation into tractable sub-problems. Using only pairwise messages of fixed size that travel at most \(O(n^{\frac{1}{3}})\) hops, our algorithm is robust and has communication cost of O(n loglogn logε − 1) transmissions, which is order-optimal up to the logarithmic factor in n. Simulated experiments verify the good expected performance on graphs of many thousands of nodes.
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Tsianos, K.I., Rabbat, M.G. (2010). Fast Decentralized Averaging via Multi-scale Gossip. In: Rajaraman, R., Moscibroda, T., Dunkels, A., Scaglione, A. (eds) Distributed Computing in Sensor Systems. DCOSS 2010. Lecture Notes in Computer Science, vol 6131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13651-1_23
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DOI: https://doi.org/10.1007/978-3-642-13651-1_23
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