AdaDP-CFL: Cluster Federated Learning with Adaptive Clipping Threshold Differential Privacy

Federated learning is a distributed machine learning approach that enables multiple clients to train models collaboratively. As its data remains stored locally on each client, this approach significantly enhances the protection of private information. However, federated learning still faces privacy leakage risks in environments with data heterogeneity. Differential privacy mechanisms are widely utilized in federated learning to ensure privacy for clients, and the magnitude of the clipping threshold directly impacts model utility. The current research does not adequately address the impact of model accuracy and training loss on clipping thresholds and is challenged by excessive hyperparameter adjustments. In response to these challenges, we propose an adaptive clipping-based differential privacy federated learning algorithm named AdaDP-CFL. It achieves model personalization and facilitates knowledge sharing among different groups through clustering and regularization techniques. Subsequently, the algorithm addresses the issue of adaptive clipping for various clients, formulated as a Markov decision process, by utilizing a deep deterministic policy gradient model based on gradient differences across client groups. Experimental results demonstrate that our algorithm outperforms current algorithms in accuracy, effectively balancing privacy protection and model utility.