Privacy Protection Algorithm for Federated Learning Based on Personalized Gradient Clipping

Federated learning, as the most commonly used privacy protection framework in deep learning, is widely applied by many institutions. The various participants in this framework achieve the goal of sharing data by uploading model parameter data without leaving the local data. However, in federated learning, privacy leakage occurs when various parties frequently upload and receive parameters. To address this issue, a personalized gradient clipping-based federated learning privacy preserving algorithm (AADP-FL) is proposed. This algorithm calculates the clipping threshold for each layer based on the L1 norm of historical data from different network layers of the participants. The gradient data is then clipped to limit the gradient range and prevent gradient explosion and vanishing gradients. Simultaneously, the contribution of each layer is calculated, privacy budgets are allocated for each layer based on their contribution, and then personalized noise is added. Participants add an appropriate amount of noise when uploading data to conceal the specific content, thereby hiding the contribution rate of each participant and improving the data security for each participant. A series of experiments reveal that the accuracy of this algorithm is superior compared to the commonly used personalized gradient clipping methods, with an accuracy increase of over 3.5 percentage points. This algorithm can also maintain a high accuracy compared with traditional federated learning frameworks. It can effectively protect the privacy of participant data while maintaining high accuracy, achieving a balance between model performance and data privacy.