PADP-FedMeta: A personalized and adaptive differentially private federated meta learning mechanism for AIoT

Powered by edge computing, the last few years have seen a rapid growth in AIoT applications. Federated learning (FL), as a typical machine learning framework for edge intelligence, has attracted a large number of attention since it can protect user privacy. However, recent studies have shown that FL cannot fully ensure privacy. To address this, differential privacy technique is widely used in FL. Nevertheless, existing works neglect that data on devices are non-independent and identically distributed (Non-IID), which largely degrades model accuracy and convergence speed. In this paper, we propose PADP-FedMeta, a personalized and adaptive differentially private federated meta learning mechanism with a provable privacy and convergence guarantee. PADP-FedMeta mitigates the negative effect of Non-IID upon model accuracy by introducing federated meta learning, and significantly improves the convergence speed with an adaptive privacy parameter. Comprehensive experimental results show the effectiveness of our mechanism and its superior performance over the state-of-the-art differentially private FL schemes.