Power Allocation and Client Selection For Privacy-Preserving Federated Learning in IoMT
Jingyuan Liu, Zheng Chang, Kai You Wang, Geyong Min · 2024
In recent years, the Internet of Medical Things (IoMT) has significantly boosted the healthcare industry. In the IoMT, federated learning (FL) can be applied, which can increase the utilization of patient data while protecting patient privacy. This work proposes a cutting-edge framework that combines differential privacy (DP) with FL and utilizes game theory to optimize power allocation and client selection in IoMT environments. Utilizing a Stackelberg game model, we orchestrate power allocation strategies among IoMT devices to enhance communication efficiency while meeting stringent privacy standards. We propose non-uniform and uniform pricing strategies based on the availability of network state information. Then, we derive the optimal interference price and power for the IoMT devices using nonlinear programming and convex optimization. In addition, our approach integrates DP to protect patients’ data, carefully balancing between privacy and the accuracy of the learning model. The conducted simulations show that our proposed method is effective in terms of communication efficiency, privacy preservation and FL performance.