A Framework for Privacy-Preserving Efficient Collaborative Learning

In recent years, federated learning has been widely used in deep learning training tasks such as image recognition. The federated learning mechanism allows multiple participants to collaboratively train a common model without having to aggregate data. However, participants may infringe on the privacy of data owners when collecting personal data. And when there are significant differences in data distribution among participants, traditional federated learning methods may yield less than ideal training results. To solve these problems, we propose an efficient privacy-preserving collaborative learning framework (EPPCL), which provides secure access authorization and training for personal data. While introducing an authorization mechanism, we design an efficient collaborative training method that can improve the model performance in cases of data heterogeneity. Furthermore, we evaluate the system performance of EPPCL. The experimental results demonstrate that EPPCL is a secure and effective way to achieve multi-party collaborative training for personal data.