HAPFL: Heterogeneity-Aware Personalized Federated Learning via Hierarchical RL and Model Distillation
Xi Chen, Ting Wang, Qin Li, Haibin Cai · 2025
Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, making it well-suited for privacy-preserving applications in heterogeneous IoT environments. However, disparities in client model architectures and computational resources often lead to accuracy degradation and the straggler problem, undermining training efficiency. To address these challenges, we propose HAPFL, a novel Heterogeneity-aware Personalized Federated Learning framework based on multi-level Reinforcement Learning (RL). HAPFL integrates three key components: 1) An RL-based model allocation mechanism that employs a PPO agent to assign appropriately sized models to clients based on their computing capabilities; 2) An RL-based training intensity adjustment scheme that dynamically controls local training epochs per client to reduce straggling latency; 3) A mutual learning scheme using knowledge distillation between each client's local model and a homogeneous lightweight model (LiteModel), which also serves as the global aggregation model to tackle model heterogeneity. Experiments on MNIST, CIFAR-10, and ImageNet-10 demonstrate that HAPFL achieves superior accuracy while reducing overall training time by$20.9 \%-40.4 \%$and straggling latency by$\mathbf{1 9. 0 \% - 4 8. 0 \%}$compared to existing approaches.