AdvPL: Adversarial Personalized Learning
Wei Du, Xintao Wu · 2020
The data generation sources are increasing in the past few years, such as mobile devices, embedded sensors, various intelligent equipment and so forth. These increasing data sources push the deployment of deep learning models in a distributed manner. However, the traditional distributed deep learning is to build a global model over all collected data and may overlook specific components which are of vital importance to personalized users. In this paper, we propose a learning framework that allows an individual user to build a personalized model. Our framework consists of two stages, including efficient similar data selection from other users and adversarial training. Instead of selecting similar data by computing hand-designed similarity metrics, we train an auto-encoder and a GAN on individual user's data, and use them to request similar data from other users. To further improve the personalized model performance, we apply adversarial training to minimize the distribution discrepancy between requested data and user's own data. Experimental results demonstrate the effectiveness of the proposed framework.