EAFL-ALP: Energy-Efficient Asynchronous Federated Learning With Adaptive Layered Personalization for Vehicular Networks
Jing Zhang, Hongming Hou, Meirun Zhang, Li Xu, Xiucai Ye · IEEE Internet of Things Journal · 2025
Federated Learning (FL) is the standard paradigm for privacy-preserving model training across distributed Industrial IoT (IIoT) devices; however, deployment remains hindered by non-IID data, high communication costs, and unstable asynchronous convergence. We present Energy-Aware Asynchronous Federated Learning with Adaptive Layered Personalization (EAFL-ALP), which achieves a 99.8% reduction in per-round traffic while improving accuracy and robustness. The framework comprises three coordinated modules: (1) Adaptive Fractal-Wave Personalisation Model (AFWPM), which for each client, grows an entropy-conditioned fractal branch and prunes it with wave-collapse, yielding a self-similar, capacity-adaptive head that captures data heterogeneity; (2) Layerwise Quantization-Based Reversible Differential Privacy Gradient Compression (LQGCM), a variance-driven block stratified that transmits 88-bit meta tuples only, enabling codebook resonance replay, invertible vector quantization and Laplace-private gradients without any numeric payload or sparsity mask; (3) Energy-Minimisation Aggregation Model (EMAM), a closed-form update that mixes staleness weights, proxy-gradient correction and EMA momentum for stable convergence on lossy links. Experiments on five IIoT benchmarks show that EAFL-ALP increases accuracy by up to 32.1%, accelerates convergence 3.3×, lowers privacy leakage by 34.1%, and reduces communication volume by two orders of magnitude with no loss of model fidelity.