FedHiPL: Federated Hierarchical Prototype Learning for Heterogeneous Non-IID Data
Abstract
Federated learning (FL) collaboratively trains models across networked industrial Internet of Things (IIoT) terminals. However, statistical heterogeneity in IIoT data often hinders the performance of global models. Current FL methods typically focus on single-level representation alignment and fail to exploit gradient-guided semantic feedback across hierarchical layers. To address statistical heterogeneity, we propose federated hierarchical prototype learning (FedHiPL), which models local representations with Gaussian prototypes and improves global learning through hierarchical prototype calibration. First, FedHiPL performs multilevel prototype alignment based on symmetric Kullback–Leibler divergence to enforce representation consistency across layers. Second, FedHiPL calibrates the local decision head by balancing local and global decision objectives with decision consistency constraints. Third, FedHiPL rectifies global prototypes through a gradient-guided hierarchical calibration module to maintain structural consistency across network layers. Experiments on a custom-constructed distributed cluster demonstrate that FedHiPL achieves 93.24% accuracy on Edge-IIoT and 72.36% accuracy on UNSW-NB15 under strong statistical heterogeneity, outperforming the representative prototype-based baseline FedProto by 6.06% and 15.80%, respectively.