linear networks). In the recent work [@CFFTZ07], Carli [*et al.*]{} proposed a distributed algorithm that uses quantized values and preserves the average at each iteration. They showed favorable convergence properties using simulations on some static topologies, and provided performance bounds for the limit points of the generated iterates. Our results on quantized averaging algorithms differ from these works in that [*we study a more general case of time-varying topologies, and provide tight polynomial bounds on both the convergence time and the discrepancy from the initial average, in terms of the number of quantization levels*]{}.

The paper is organized as follows. In Section \[agreementalg\], we introduce a general class of averaging algorithms, and present our assumptions on the algorithm parameters and on the information exchange among the agents. In Section \[convtimesection\], we present our main result on the convergence time of the averaging algorithms under consideration. In Section \[matrixpicking\], we present a distributed averaging algorithm for the case of undirected graphs, which picks the weights dynamically, resulting in an improved bound on the convergence time. In Section \[qanalysis\], we propose and analyze a quantized version of the averaging algorithm. In particular, we establish bounds on the convergence time of the iterates, 