Mining frequent subgraphs in large scale graph data sets helps reveal underlying knowledge. Since the mining approaches in centralized systems are often bottlenecked on calculation capacity, many parallelized solutions based on the MapReduce framework are proposed to scale out the mining process, which usually extracts frequent subgraphs in an iterative way. Nonetheless, the efficiency and scalability of these MapReduce based approaches are still bounded by the communication cost for passing the intermediate results and the unbalanced workload after a few iterations. In this paper, we propose an efficient and scalable framework for frequent subgraph mining by using distributed graph processing systems. It adopts a message-passing-free scheme among workers to reduce the communication cost, and utilizes a task scheduler to dynamically balance the workload. Experimental results on both synthetic and real-world data sets verify the efficacy of our proposed framework.