guarantees the convergence of the values of the agents to a common value. In particular, we prove that all agents have the same value after $O((n^2/\eta)\log (nQ))$ time steps, where $Q$ is the number of quantization levels per unit value. Due to the rounding-down feature of the quantizer, this algorithm does not preserve the average of the values at each iteration. However, we provide bounds on the error between the final consensus value and the initial average, as a function of the number $Q$ of available quantization levels. In particular, we show that the error goes to 0 at a rate of $(\log
Q)/Q$, as the number $Q$ of quantization levels increases to infinity.

Other than the papers cited above, our work is also related to [@KBS06] and [@CFSZ05; @CFFTZ07], which studied the effects of quantization on the performance of averaging algorithms. In [@KBS06], Kashyap [*et al.*]{} proposed randomized [*gossip-type*]{} quantized averaging algorithms under the assumption that each agent value is an integer. They showed that these algorithms preserve the average of the values at each iteration and converge to approximate consensus. They also provided bounds on the convergence time of these algorithms for specific static topologies (fully connected and 