Federated Learning (FL) is vital in distributed systems, especially for ensuring data privacy, particularly in IoT and edge-based setups. However, existing research mainly focuses on data heterogeneity, leaving gaps in addressing varying device capabilities and communication efficiency. To bridge this, we propose the “Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments (REFT)”. REFT leverages Variable Pruning to adapt pruning strategies to client computational capabilities, enhancing resource utilization. Additionally, our approach employs knowledge distillation to reduce bidirectional client-server communication, reducing bandwidth usage. Experimentation in image classification tasks demonstrates the effectiveness of REFT in resource-limited environments. Our method preserves data privacy and performance standards while accommodating diverse client devices, offering a minimal bandwidth solution for FL-based systems.