ADP-QFed: Privacy-Preserving Quantized Federated Learning for Intelligent Edge Sensing IoT Systems

Federated learning (FL) enables decentralized model training but faces critical challenges in jointly optimizing privacy, accuracy, and communication efficiency, essential for resource-constrained wireless internet of things (IoT) deployments. We introduce an adaptive differentially private quantized FL (ADP-QFed) framework that addresses these challenges through layer-wise adaptive noise injection and dual-bit deterministic quantization. By computing layer-specific sensitivity and importance scores, ADP-QFed dynamically calibrates privacy noise to minimize accuracy loss while ensuring rigorous (δ )-differential privacy (DP) guarantees. The framework employs n-bit quantization for local computation and m-bit quantization for transmission, reducing communication overhead by up to 75%. Experiments on MNIST, FMNIST, and CIFAR-10 achieve test accuracies of 99.41%, 91.06%, and 82.94%, respectively, outperforming existing privacy-preserving FL methods by an average of 3.5%. These results are obtained while maintaining a privacy budget under = 2.25 , representing a 40% reduction compared to state-of-the-art methods at similar accuracy levels. ADP-QFed advances practical privacy-preserving FL for edge sensing in low-altitude IoT systems by simultaneously optimizing privacy guarantees, model utility, and energy efficiency in wireless environments.