Scalable Average Consensus with Compressed Communications
This addresses communication efficiency in distributed systems, though it appears incremental as an extension of consensus algorithms with compression.
The paper tackles the problem of decentralized average consensus with compressed communications, proposing an algorithm that scales linearly with network size and converges to the average of initial values for a broad class of compression operators over arbitrary static networks.
We propose a new decentralized average consensus algorithm with compressed communication that scales linearly with the network size n. We prove that the proposed method converges to the average of the initial values held locally by the agents of a network when agents are allowed to communicate with compressed messages. The proposed algorithm works for a broad class of compression operators (possibly biased), where agents interact over arbitrary static, undirected, and connected networks. We further present numerical experiments that confirm our theoretical results and illustrate the scalability and communication efficiency of our algorithm.