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.

翻译：本文提出了一种基于生成对抗网络与联邦学习的模型，以应对智能预测与推荐应用中的诸多挑战，例如高响应时间、数据隐私受损以及数据稀缺等问题。生成对抗网络与联邦学习的结合被称为生成式联邦学习。作为所提模型的案例研究，本文探讨了心脏健康监测应用。通过基于生成对抗网络的算法生成逼真的合成数据集，以提升数据多样性、数据质量并实现数据增强，从而解决数据稀缺与类别不平衡问题。本文在边缘计算范式中实现了集中式与去中心化联邦学习方法。在集中式联邦学习中，边缘节点与中央服务器通信，以协作方式构建全局模型与个性化局部模型。在去中心化联邦学习方法中，边缘节点之间相互通信，交换模型更新以进行协作训练。对比研究表明，所提框架优于现有的心脏健康监测应用。结果显示，使用所提框架（i）预测准确率较传统框架提升12%，（ii）响应时间较纯云系统降低73%。