Gossip Algorithms

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How researchers see this paper

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Decentralized Collaborative Learning of Personalized Models over Networks (2016, background): “Gossip algorithms are known to be very effective for decentralized computation in peer-topeer networks (see Dimakis et al., 2010; Shah, 2009).”
Extending Gossip Algorithms to Distributed Estimation of U-statistics (2015, background): “For a detailed account of the literature on gossip algorithms, we refer the reader to [19, 6].”
Cloud robotics: architecture, challenges and applications (2012, background): “It can be shown [8] that the communication delay of disseminating a message from a single node in the M2M network to all N nodes in the network is O(log N/Φ), where Φ is the conductance of the network, given by Φ = min S:|S|≤N/2 i∈S,j∈S c P ij |S| .”
A lecture on the averaging process (2012, background): “In the general framework, each agent i is in some state Xi(t) at time t, and the state can only change when agent i meets some other agent j, at which time the two agents’ states are updated according to some rule (deterministic or random) which depends only on the pre-meeting states Xi(t−), Xj(t−).”