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Federated Learning (FL) has been widely used to train shared machine learning models while addressing the privacy concerns. When deployed in wireless networks, bandwidth resources limitation is a key issue, thereby necessitating device scheduling and bandwidth allocation. It is challenging to carry out device scheduling due to the large combinatorial search space. Besides, the heterogeneous computing capabilities and uncertain channel states of wireless devices complicate the design of a bandwidth allocation method. In this paper, we propose a joint device scheduling and bandwidth allocation framework for implementing FL in wireless networks. Specifically, deep reinforcement learning (DRL) is employed to conduct device scheduling. To this end, the state space, action space, and reward function of DRL are carefully defined for a typical FL system. Long short-term memory (LSTM) is adopted as the DRL …