Finding sparse cuts is an important tool in analyzing large-scale distributed networks such as the Internet and Peer-to-Peer networks, as well as large-scale graphs such as the web graph, online social communities, and VLSI circuits. In distributed communication networks, they are useful for topology maintenance and for designing better search and routing algorithms. In this paper, we focus on developing fast distributed algorithms for computing sparse cuts in networks. Given an undirected $n$-node network $G$ with conductance $φ$, the goal is to find a cut set whose conductance is close to $φ$. We present two distributed algorithms that find a cut set with sparsity $\tilde O(\sqrtφ)$ ($\tilde{O}$ hides $\polylog{n}$ factors). Both our algorithms work in the CONGEST distributed computing model and output a cut of conductance at most $\tilde O(\sqrtφ)$ with high probability, in $\tilde O(\frac{1}{b}(\frac{1}φ + n))$ rounds, where $b$ is balance of the cut of given conductance. In particular, to find a sparse cut of constant balance, our algorithms take $\tilde O(\frac{1}φ + n)$ rounds. Our algorithms can also be used to output a {\em local} cluster, i.e., a subset of vertices near a given source node, and whose conductance is within a quadratic factor of the best possible cluster around the specified node. Both our distributed algorithm can work without knowledge of the optimal $φ$ value and hence can be used to find approximate conductance values both globally and with respect to a given source node. We also give a lower bound on the time needed for any distributed algorithm to compute any non-trivial sparse cut --- any distributed approximation algorithm (for any non-trivial approximation ratio) for computing sparsest cut will take $\tilde Ω(\sqrt{n} + D)$ rounds, where $D$ is the diameter of the graph.