Executing Federated Learning (FL) efficiently within heterogeneous Computing Power Networks (CPNs) presents a critical challenge. Traditional FL methods, designed for homogeneous environments, utilize static, resource-blind scheduling. This leads to severe performance bottlenecks and prohibitive training latency when confronted with the multi-dimensional resource (CPU, memory, bandwidth) and data (Non-IID) heterogeneity inherent in CPNs, as system efficiency is dictated by the slowest clients or “stragglers.“ To address this, we propose FedDRL, a novel FL framework featuring a resource-aware intelligent scheduler based on Deep Reinforcement Learning (DRL). We model the complex scheduling task as a sequential decision-making process. A DRL agent on the control plane perceives the real-time global state of the CPN, including client-side computational capabilities and data characteristics, to learn a dynamic task assignment policy. This policy tailors the computational load for each client in every round, aiming to minimize long-term average training latency without sacrificing model performance. Extensive experiments on benchmark datasets, including CIFAR-10 and the more challenging CIFAR-100, demonstrate that FedDRL significantly outperforms baseline algorithms. Notably, under extreme data heterogeneity (α = 0.1) on CIFAR-10, our method reduces end-to-end training latency by an order of magnitude while achieving state-of-the-art accuracy, a performance advantage that is consistently validated on CIFAR-100. This work provides an adaptive, self-optimizing solution for deploying efficient Federated Learning in complex, real-world Computing Power Network environments.