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Federated learning is considered as a privacy-preserving distributed machine learning framework, where the model training is distributed over end devices by fully exploiting scattered computation capability and training data. Different from centralized machine learning where the convergence time is decided by number of training rounds, under the framework of FL, the convergence time also depends on the communication delay and computation delay for local training in each round. Therefore, we employ total training delay as the performance metric in our strategy design. Note that the training delay per round is prone to the limited wireless resources and system heterogeneity, where end devices have different computational and communication capabilities. To achieve timely parameter aggregation over limited spectrum, we incorporate age of parameter in device scheduling for each training round, which is …