The deployment of deep learning algorithms has significantly enhanced swarm intelligence. However, pre-trained models often perform poorly in dynamic and unpredictable environments. While online training with distributed updates offers a promising solution, the heterogeneity of computing resources in swarm systems poses substantial challenges for efficient model updating. To address this, we propose a Distributed Training Scheduler for Heterogeneous Swarms (DTSHS), a reinforcement learning-based scheduling framework that dynamically balances workload across devices. DTSHS aims to optimize the trade-off between training time and energy consumption by learning adaptive task offloading strategies. Experimental results demonstrate that DTSHS outperforms traditional methods in both time and energy efficiency.