Abstract: Joint-sparse recovery from multiple measurement vectors has to transfer all the measurement vectors obtained from different nodes or sensors to fusion center (maybe far away from individual nodes). However, collecting all the data to fusion center (FC) may be challenging or impossible, especially in the cases that the power and computing resources are limited, or there is no FC. To overcome the above problem, a distributed pathwise coordinate optimization algorithm is developed to solve joint-sparse from multiple measurement vectors (MMV). Benefiting from the distributed computation and pathwise coordinate optimization, the new algorithm entails low computation and power overhead, and affordable data transferring for each node among its neighbors. Simulation results show that the new distributed algorithm is very competitive to the centralized algorithm.