Green Federated Learning with Adaptive Model Pruning for Sustainable Edge Intelligence in Smart Industries
Due to the rapid proliferation of Industrial Internet of Things (IIoT) systems within smart industries, scalable, energy-efficient, and sustainable edge intelligence solutions are in demand. Despite the fact that Federated Learning (FL) allows collaborative training without raw data sharing, traditional FL methods have high communication overhead, huge model sizes, and require a lot of energy, which restricts their implementation in resource-constrained industrial settings. To address these issues, the present paper introduces a Green Federated Learning framework, GFed-AMP, a sustainable IIoT edge intelligent framework that includes Adaptive Model Pruning.The suggested framework incorporates the dynamic magnitude-based pruning of local updates to eliminate redundant parameters and lessen the computational complexity. A strategy of energy-conscious client selection assigns priority to the devices with larger residual energy and with lower carbon intensity, to make sure that participation is environmentally conscious. It has real-time energy and carbon monitoring modules that can be used to gauge power consumption and environmental effects during federated training. Moreover, a sparse-awares aggregation mechanism is an effective method which optimally manages pruned model updates and ensures stable convergence. Experimental assessment of industrial anomaly detection data sets shows that GFed-AMP can achieve up to 40% model reduction, 35% communication overhead reduction, and 30% overall energy reduction in comparison with traditional FedAvg. Notably, the decline in the accuracy of predictions is less than 1.5%, which proves robustness and stability of learning. Better scalability, bandwidth, and lower carbon emission are proven by statistical comparisons. All in all, the overall experience with GFed-AMP’s trade-off between accuracy, communication efficiency, and environmental sustainability is a viable green AI solution to Industry 4.0 deployments.