While Distributed POMDPs have become popular for modeling multiagent systems in uncertain domains, it is the Network Distributed POMDPs (ND-POMDPs) model that has begun to scale-up the number of agents. The ND-POMDPs can utilize the locality in agents' interactions. However, prior work in ND-POMDPs has failed to address communication. Without communication, the size of a local policy at each agent within the ND-POMDPs grows exponentially in the time horizon. To overcome this problem, we extend existing algorithms so that agents periodically communicate their observation and action histories with each other. After communication, agents can start from new synchronized belief state. Thus, we can avoid the exponential growth in the size of local policies at agents. Furthermore, we introduce an idea that is similar the Point-based Value Iteration algorithm to approximate the value function with a fixed number of representative points. Our experimental results show that we can obtain much longer policies than isting algorithms as long as the interval between communications is small.