Generative Federated Learning for Smart Prediction and Recommendation Applications

TL;DR

This paper introduces Generative Federated Learning (GFL), combining GANs and federated learning to enhance smart prediction and recommendation systems by addressing data privacy, scarcity, and response time issues, demonstrated through a heart health monitoring case study.

Contribution

The paper presents a novel GFL framework integrating GANs with federated learning for improved data augmentation, privacy, and model performance in edge computing applications.

Findings

Prediction accuracy improved by 12% over traditional methods.

Response time reduced by 73% compared to cloud-only systems.

Synthetic data generation enhances data diversity and quality.

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.