Generative Federated Learning for Smart Prediction and Recommendation Applications

Abstract

This paper proposes a generative adversarial network and federated learning-based model to address various challenges of the smart prediction and recommendation applications, such as high response time, compromised data privacy, and data scarcity. The integration of the generative adversarial network and federated learning is referred to as Generative Federated Learning (GFL). As a case study of the proposed model, a heart health monitoring application is considered. The realistic synthetic datasets are generated using the generated adversarial network-based proposed algorithm for improving data diversity, data quality, and data augmentation, and remove the data scarcity and class imbalance issues. In this paper, we implement the centralized and decentralized federated learning approaches in an edge computing paradigm. In centralized federated learning, the edge nodes communicate with the central server to build the global and personalized local models in a collaborative manner. In the decentralized federated learning approach, the edge nodes communicate among themselves to exchange model updates for collaborative training. The comparative study shows that the proposed framework outperforms the existing heart health monitoring applications. The results show that using the proposed framework (i) the prediction accuracy is improved by 12% than the conventional framework, and (ii) the response time is reduced by 73% than the conventional cloud-only system.