A Review of Lightweight Federated Learning Research for Home Edge IoT
A co-optimization research framework is proposed, incorporating pruning rate, quantization bit width, and aggregation weights into a unified optimization space, to strike a balance among global model accuracy, training energy consumption, and convergence speed under device resource constraints.