Before revision:  We construct the communication structure with Long-Short Term Memory (LSTM) cells and star graphs, in which the computational complexity of the proposed learning algorithm scales linearly with the number of robots and suitable for application with a large number of robots. The proposed methodology is validated with a map classification problem in the simulated environment. It is shown that the proposed architecture achieves a comparable classification accuracy with the centralized methods, maintains high performance with various numbers of robots without additional training cost, and robust to hacking and loss of the robots in the network .
After revision:  We construct the communication structure with Long-Short Term Memory (LSTM) cells and star graphs, in which the computational complexity of the proposed learning algorithm scales linearly with the number of robots ). Our approach is particularly useful for fast map classification in large environments using a large number of communicating robots. We validate the usefulness of our proposed methodology through extensive simulations .