Federated Deep Reinforcement Learning (FDRL) aims to enable distributed collaborative training of multiple DRL models while preserving privacy. Existing FDRL methods function in static client environments, but real-world scenarios often involve dynamic state transitions, such as noise, which render static model topologies inadequate and result in biased policy loss. This degrades client performance and leads to suboptimal global policies. To address this challenge, we develop a generic solution, referred to as the self-regulating training framework, which can be seamlessly integrated into existing FDRL approaches to address dynamic state transitions. Specifically, we propose a Sparse Training (ST) method that dynamically sparsifies and adjusts the topology of each model during training to maximize model performance and reduce model complexity. Additionally, we introduce an auxiliary model to adaptively regulate the policy loss of client models, mitigating loss bias and facilitating updates that yield improved returns. Experimental results demonstrate that our method enhances six state-of-the-art (SOTA) FDRL approaches across nine tasks in terms of return.