Energy-efficient federated deep learning for edge-based image recognition in resource-constrained IoT environments
Experimental evaluation on MNIST, CIFAR-10, and Tiny ImageNet under a controlled federated simulation indicates that the proposed Federated CNN attains classification accuracy within 2.1 percentage points of a centralized baseline and reduces average per-round energy by approximately 43% relative to centralized trainin...