We have been investigating the performance improvements achievable in quantum machine learning applications. Quantum reinforcement learning, exemplified by its communication efficiency gains in federated learning, stands out as a positive outcome. However, it's important to note that not all quantum machine learning tasks exhibit comparable results, as the reduction in the number of parameters may be impacted by model convergence. Furthermore, the training of federated reinforcement learning, characterized by changes in algorithmic logic and update strategy, experienced some efficiency degradation compared to reinforcement learning training. We aim to address and enhance these aspects in our future work