Atlas: Ensuring Accuracy for Privacy-Preserving Federated IoT Applications
Jiechao Gao, Mingyue Tang, Wenpeng Wang, Tushar Routh, Bradford Campbell · 2025
In smart Internet of Things (IoT) applications, edge devices often collect and store limited data, which is insufficient for training modern deep learning models. Collaborative training methods like cloud computing and federated learning enable robust models for IoT applications, yet introduce data privacy concerns due to central data collection and model inversion attacks. Remedies such as differential privacy can bring data privacy protection but dramatically degrade the accuracy performance of IoT applications. To safeguard user data privacy while maintaining application quality, it is imperative to establish a framework capable of preserving user privacy without compromising accuracy standards.