To cope with the impact of different device computing capabilities and non-independent uniformly distributed data on federated learning performance. and to efficiently schedule terminal devices to complete model aggregation. a method of node selection based on deep reinforcement learning was proposed. It considered training quality and efficiency of heterogeneous terminal devices. https://www.roneverhart.com/Aftermath-Cabernet-Sauvignon-Napa-Valley-2017/