Federated Twin Support Vector Machine

Abstract

TSVM is designed to solve binary classification problems with less computational overhead by finding two hyperplanes and has been widely used to solve real-world problems. However, in real scenarios, the data used for learning is always scattered in different institutions or users. At the same time, people pay more attention to the issue of personal privacy leakage. Due to the complex privacy protection issues, simply collecting all the data for model training is no longer acceptable. Federated learning has recently been proposed to solve this problem. It completes model training by sharing model parameter updates in the form of data that remains local. But there is still no algorithm for twin support vector machines under the framework of federated learning. Combining the characteristics of twin support vector machine and federated learning, this paper proposes a federated twin support vector machine algorithm (FTSVM) and extends the twin support vector machine based on stochastic gradient descent into a federated support vector machine. We propose a unique initialization algorithm and integration algorithm to ensure the accuracy of the algorithm and the effectiveness of privacy protection. Accuracy experiments are carried out on five datasets, and the accuracy is basically the same as that of the TSVM based on gradient descent. Ablation experiments show that as the number of participants increases, the accuracy of the FTSVM is significantly higher than the average accuracy of the Stand-alone TSVM. Time experiments show that the time overhead of FTSVM increases linearly with the number of participants. These experiments demonstrate the accuracy, effectiveness, and possibility of application in real-world scenarios of our proposed FTSVM.