首页|Study Results from Sun Yat-sen University Update Understanding of Machine Learning (Lds-fl: Loss Differential Strategy Based Federated Learning for Privacy Preserving)
Study Results from Sun Yat-sen University Update Understanding of Machine Learning (Lds-fl: Loss Differential Strategy Based Federated Learning for Privacy Preserving)
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NETL
NSTL
IEEE
Current study results on Machine Learning have been published. According to news originating from Shenzhen, People’s Republic of China, by NewsRx correspondents, research stated, “Federated Learning (FL) has attracted extraordinary attention from the industry and academia due to its advantages in privacy protection and collaboratively training on isolated datasets. Since machine learning algorithms usually try to find an optimal hypothesis to fit the training data, attackers also can exploit the shared models and reversely analyze users’ private information.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Sun Yat-sen University, “However, there is still no good solution to solve the privacy-accuracy trade-off, by making information leakage more difficult and meanwhile can guarantee the convergence of learning. In this work, we propose a Loss Differential Strategy (LDS) for parameter replacement in FL. The key idea of our strategy is to maintain the performance of the Private Model to be preserved through parameter replacement with multi-user participation, while the efficiency of privacy attacks on the model can be significantly reduced. To evaluate the proposed method, we have conducted comprehensive experiments on four typical machine learning datasets to defend against membership inference attack. For example, the accuracy on MNIST is near 99%, while it can reduce the accuracy of attack by 10.1% compared with FedAvg.”
ShenzhenPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningSun Yat-sen University
NETL
NSTL
IEEE
