Depression Detection Using Asynchronous Federated Optimization

With the rapid growth of population, the life pressure, and the various intensive pressures every day deepen the competition among people more intensive. Tens of millions of people have been suffering from depression every year and only a fraction receives adequate treatment. The development of social networks such as Facebook, Twitter, Weibo, and QQ provides more convenient communication and provides a new emotional release window. People communicate with their friends and share their opinions to express their feelings. It provides an opportunity to detect depression in social networks. Although using social networks to detect depression has picked an established position on a global scale, few researchers consider the data security and privacy-preserving schemes. Therefore, we propose the federated learning technique as an efficient and scalable method. With the larger number of social networks data from diverse edge devices, federated learning can process a large number of edge devices in parallel. For the study, we aim to analyze depression on Weibo collected from an online social network. We propose a novel algorithm Text-CNN Asynchronous Federated optimization (CAFed) based on federated learning to improve the communication cost and convergence rate. We have shown our proposed method can effectively protect users' privacy under the premise of ensuring the accuracy of prediction. We prove that our proposed method's convergence rate is faster than the Federated Averaging (FedAvg) for non-convex problems. Federated learning techniques identify high-quality solutions to mental health issues among Weibo users.