Enhancing personalized modeling via weighted and adversarial learning

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 individual users. In this paper, we propose an adversarial 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 generative adversarial network (GAN) on individual user’s data and use them to request similar data from other users. To further improve the personalized model performance, we develop two approaches that combine the requested data and user’s own data to build the personalized model. The first approach is that we apply weighted learning to capture the different importance of the requested data. The second approach is that we apply adversarial training to minimize the distribution discrepancy between the requested data and user’s own data. Experimental results demonstrate the effectiveness of the proposed framework.