Intent-Driven Dual-Layer Model Pruning for Energy-Efficient Hierarchical Federated Learning in IoT With Non-IID Data

The proliferation of Internet of Things (IoT) devices has intensified the need for scalable and energy-efficient federated learning (FL). While Hierarchical Federated Learning (HFL) improves scalability by adding an edge aggregation tier, it still suffers from high communication costs, slow convergence, and degraded accuracy under non-IID data. Existing methods such as quantization, sparsification, and static pruning alleviate specific bottlenecks but fail to jointly optimize efficiency, robustness, and accuracy. This paper proposes an intent-driven dual-layer model pruning framework for HFL, where an Energy Management System (EMS) and an Intent-driven Pruning Orchestrator (IDPO) dynamically translate system-level intents (e.g., energy minimization or accuracy preservation) into pruning actions at both edge and cloud layers. Experiments on MNIST, CIFAR-10, and FEMNIST show up to 41% smaller models, 12× faster training, 28–35% lower energy use, and +12.9% accuracy gain under non-IID data, establishing the framework as a robust and sustainable solution for IoT learning.