Energy-Efficient Federated Learning via Dynamic Distillation and Cloud-Network Collaboration

The extensive local training in Hierarchical Federated Learning (HFL) imposes a substantial computational energy burden on end devices, a problem intensified by inherent system and data heterogeneity. While prior works attempt to mitigate this by using heterogeneous models or adjusting local training, they often suffer from critical drawbacks such as accuracy degradation from update biases and an inability to adapt to the dynamic nature of device resources. This paper introduces FedE2AD (Federated Energy-Efficient Adaptive Distillation), a novel cloud-network collaboration framework that leverages dynamic distillation to holistically optimize the energy-accuracy balance. At its core, FedE2AD implements this collaboration through a multi-level optimization approach. At the cloud layer, a Dynamic Model Allocation strategy intelligently assigns model architectures by assessing device status from static, dynamic, and data-centric perspectives. At the device layer, Variable Local Iterations enable real-time adaptation to fluctuating computational power. Crucially, to counteract model divergence, FedE2AD employs a Dual Knowledge Sharing mechanism at the edge layer, which uniquely combines direct aggregation of shared structures with data-free model distillation to ensure robust knowledge transfer. Experiments conducted on simulation platforms show that FedE2AD markedly outperforms existing methods. For instance, on the CIFAR-10 dataset under strong heterogeneity, it reduces single-round computation energy by 21.1% and increases final model accuracy by 1.42% compared to HDHRFL.