Title: Distributed Zeroth-Order Optimization: Convergence Rates That Match Centralized Counterpart. Abstract: Zeroth-order optimization has become  increasingly important  in complex optimization and machine learning  when  cost functions are impossible to be described   in closed analytical forms. The key idea of zeroth-order optimization lies in the ability for a learner to build gradient estimates by queries  sent to the cost function, and then traditional  gradient descent algorithms can be executed with gradients replaced by the estimates. For optimization of large-scale  multi-agent systems with decentralized data and costs, zeroth-order optimization can continue to be utilized to develop scalable and distributed zeroth-order algorithms. It is important to understand the trend in performance transitioning from centralized to distributed zeroth-order algorithms in terms of convergence rates, especially for multi-agent systems  with time-varying communication networks. In this paper, we  establish a series of convergence rates for distributed zeroth-order subgradient algorithms under both one-point and two-point zeroth-order oracles. Apart from the additional node-to-node communication cost in distributed algorithms, the established rates in convergence are shown to match their centralized counterpart. We also propose a multi-stage distributed zeroth-order algorithm that better utilizes the learning rates, reduces the computational complexity,   and attains even faster convergence rates for compact decision set.