TY  - EJOU
AU  - Jiao, Sanxiu 
AU  - Cai, Lecai 
AU  - Meng, Jintao 
AU  - Zhao, Yue 
AU  - Cheng, Kui 

TI  - Efficient DP-FL: Efficient Differential Privacy Federated Learning Based on Early Stopping Mechanism
T2  - Computer Systems Science and Engineering

PY  - 2024
VL  - 48
IS  - 1
SN  - 

AB  - Federated learning is a distributed machine learning framework that solves data security and data island problems faced by artificial intelligence. However, federated learning frameworks are not always secure, and attackers can attack customer privacy information by analyzing parameters in the training process of federated learning models. To solve the problems of data security and availability during federated learning training, this paper proposes an Efficient Differential Privacy Federated Learning Algorithm based on early stopping mechanism (Efficient DP-FL). This method inherits the advantages of differential privacy and federated learning and improves the performance of model training while protecting the parameter information uploaded by the client during the training process. Specifically, in the federated learning framework, this article uses an adaptive DP-FL method for gradient descent training, which makes the model converge faster than traditional stochastic gradient descent. In addition, due to model convergence, noise should be reduced accordingly. This paper introduces an early stopping mechanism to improve data availability. This paper demonstrates the performance improvement of the Efficient DP-FL algorithm through simulation experiments on real MNIST and Fashion-MNIST datasets. Experimental show that the efficient DP-FL algorithm is significantly superior to other algorithms.
KW  - Differential privacy; federated learning; data security; artificial intelligence

DO  - 10.32604/csse.2023.040194