Nowadays, federated learning (F1) has been proposed as an emerging technology to store sensory data and train the edge networks using a set of computing devices with minimum training time. However, a collection of heterogeneous participating devices with different processing power and energy usage are used to analyze the model parameters locally. Therefore, an intelligent service provisioning mechanism with the F1 technique needs to be developed at the edge networks. This strategy can increase the security and privacy of the network while minimizing the training time on resource-constrained edge devices. In this magazine, we describe the importance of the FL-aided hybrid edge intelligent framework for next-generation Internet of Things applications. Moreover, to enhance the critical service provisioning functionality, we highlight two use case studies along with their potential research directions, including intelligent transportation systems and intelligent healthcare systems. Finally, this work concludes with a set of potential future research directions of FL-aided edge networks.

中文翻译：

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用于智能服务的联邦学习辅助下一代边缘网络


如今，联邦学习（F1）已被提出作为一种新兴技术，用于存储传感数据并使用一组计算设备以最短的训练时间训练边缘网络。然而，具有不同处理能力和能源使用的异构参与设备的集合被用来在本地分析模型参数。因此，需要在边缘网络开发采用F1技术的智能业务提供机制。该策略可以提高网络的安全性和隐私性，同时最大限度地减少资源受限的边缘设备上的训练时间。在本杂志中，我们描述了 FL 辅助混合边缘智能框架对于下一代物联网应用的重要性。此外，为了增强关键服务提供功能，我们重点介绍了两个用例研究及其潜在研究方向，包括智能交通系统和智能医疗系统。最后，这项工作总结了 FL 辅助边缘网络的一系列潜在的未来研究方向。