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A Differential Privacy Federated Learning Scheme Based on Adaptive Gaussian Noise

Computer Modeling in Engineering & Sciences 2024年第2期138卷 1679-1694页

作者： sanxiu jiao Lecai Cai Xinjie Wang Kui Cheng Xiang Gao College of Automation and Information Engineering Sichuan University of Science and EngineeringYibin644000China Sanjiang Research Institute of Artificial Intelligence and Robotics Yibin UniversityYibin644000China College of Mechanical Engineering Sichuan University of Science and EngineeringYibin644000China

As a distributed machine learning method,federated learning(FL)has the advantage of naturally protecting data *** keeps data locally and trains local models through local data to protect the privacy of local *** federated learning method effectively solves the problem of artificial Smart data islands and privacy protection ***,existing research shows that attackersmay still steal user information by analyzing the parameters in the federated learning training process and the aggregation parameters on the server *** solve this problem,differential privacy(DP)techniques are widely used for privacy protection in federated ***,adding Gaussian noise perturbations to the data degrades the model learning *** address these issues,this paper proposes a differential privacy federated learning scheme based on adaptive Gaussian noise(DPFL-AGN).To protect the data privacy and security of the federated learning training process,adaptive Gaussian noise is specifically added in the training process to hide the real parameters uploaded by the *** addition,this paper proposes an adaptive noise reduction *** the convergence of the model,the Gaussian noise in the later stage of the federated learning training process is reduced *** paper conducts a series of simulation experiments on realMNIST and CIFAR-10 datasets,and the results show that the DPFL-AGN algorithmperforms better compared to the other algorithms.

关键词： Differential privacy federated learning deep learning data privacy

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