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

Omer Tariq, Muhammad Bilal Akram Dastagir, Dongsoo Han

Abstract: 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 ( $\varepsilon $ , $\delta $ )-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 $\varepsilon = 2.25$ , 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.

External IDs:dblp:journals/iotj/TariqDH26