EAFL-ALP: Energy-Efficient Asynchronous Federated Learning With Adaptive Layered Personalization for Vehicular Networks

Federated learning (FL) is the standard paradigm for privacy-preserving model training across distributed Industrial Internet of Things (IIoT) devices; however, deployment remains hindered by non-IID data, high communication costs, and unstable asynchronous convergence. We present energy-aware asynchronous FL 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 personalization 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 DP gradient compression model (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; and 3) energy minimization aggregation model (EMAM), a closed-form update that mixes staleness weights, proxy-gradient correction and exponential moving average (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 $3.3{\times }$ , lowers privacy leakage by 34.1%, and reduces communication volume by two orders of magnitude with no loss of model fidelity.