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With the rapid development of Wireless Computing Power Networks (WCPNs), the urgent need for data privacy protection and communication efficiency has led to the emergence of the federated learning (FL) framework. However, the time delay leads to dragging problems and reduces the convergence performance of FL in the training process. In this article, we propose an FL resource scheduling strategy based on information age perception in WCPNs, which can effectively reduce the time delay and enhance the convergence performance of FL. Moreover, a data cache buffer and a model cache buffer are set up at the user end and the central server, respectively. Next, we formulate the parametric age-aware problem to simultaneously minimize the global parameter age, energy consumption, and FL service delays. Considering the dynamic WCPN environment, the optimization target is modeled as a Markov decision process (MDP), and the Proximal Policy Optimization (PPO) algorithm is used to achieve the optimal solution. Numerical simulation results demonstrate that the proposed method significantly outperforms baseline schemes across critical metrics. Specifically, the proposed approach reduces FL service delays by 25.2%. It also decreases the global parameter age by 45.5% through the joint optimization of the data collection frequency, computation frequency, and bandwidth allocation. The method attains a reward value of 65 at convergence, 18.2% higher than the WithoutAnyCache scheme and 8.3% higher than the OnlyLocalCache scheme. FL accuracy improves to 98.2% with a 0.08 final loss. Finally, numerical simulation results further confirm the superiority and outstanding performance of the proposed method. Full article