Quantized Consensus by Means of Gossip Algorithm

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D2: Decentralized Training over Decentralized Data (2018, background): “In contrast, decentralized algorithms can work on any connected network and only rely on the information exchange between neighbor workers [Kashyap et al., 2007, Lavaei and Murray, 2012, Nedic et al., 2009].”
An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination (2012, background): “It is important to quantify the effects of the asynchronous communication on the consensus problem.”
Communication Compression for Decentralized Training (2018, methodology): “Other compression methods can be found in Kashyap et al. [2007], Lavaei and Murray [2012], Nedic et al. [2009].”
Consensus in multi-agent systems: a review (2021, background): “Reaching consensus in MASs with communication constraints by means of a connected weighted graph has been explored in Lavaei and Murray (2011).”
Design and analysis of distributed averaging with quantized communication (2014, methodology): “Stochastic quantized gossip algorithms were introduced in [20], [31] and shown to work properly.”
Decentralization Meets Quantization (2018, background): “[2007], Lavaei and Murray [2012], Nedic et al. [2009], but may not be suitable for our problem since they require the model on each work needs to be quantized.”