PDFed-ALD: Adaptive Primal–Dual Federated Learning Under Industrial Internet of Things

Federated learning (FL) is a distributed training paradigm that enables multiple devices in the Industrial Internet of Things (IIoT) to collaboratively train a global model without sharing private data. However, non-IID data in FL leads to client drift, which significantly degrades the performance of the global model in IIoT scenarios. While the primal–dual update method effectively mitigates client drift through dynamic regularization, optimizing the global model remains a significant challenge in IIoT due to the high degree of data heterogeneity. To address this challenge, we propose a novel FL method, PDFed-ALD, which effectively mitigates client drift and improves global model’s performance under high data heterogeneity. The core of PDFed-ALD is adaptive local distillation mechanism, which employs an adaptive distillation temperature based on the relative degree of data heterogeneity, dynamically correcting gradient updates, alleviating the issue of client drift. Furthermore, to reduce variance among local gradients, PDFed-ALD introduces a momentum-based minimum sharpness gradient correction method, which enhances local consistency by minimizing the variance between gradients across clients. Extensive experiments on image classification tasks using CIFAR-10, CIFAR-100, and MVTEC datasets demonstrate that PDFed-ALD outperforms state-of-the-art (SOTA) methods in terms of both accuracy and convergence speed across various settings, including client scale, participation rate, and degree of data heterogeneity.