Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism
Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism
复制标题
边缘网络联合学习：资源优化与激励机制
DOI：
10.1109/mcom.001.1900649
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发表时间：
2020-10-01
影响因子：
11.2
通讯作者：
Hong, Choong Seon
中科院分区：
文献类型：
--
作者：
Khan, Latif U.;Pandey, Shashi Raj;Hong, Choong Seon
近年来，智能物联网 (IoT) 设备迅速普及。具有智能的物联网设备需要使用有效的机器学习范例。联邦学习可能是实现基于物联网的智能应用程序的一个有前景的解决方案。在本文中，我们介绍了在网络边缘实现联邦学习的主要设计方面。我们通过 Stackelberg 游戏对全球服务器和联邦学习参与设备之间基于激励的交互进行建模，以激励设备参与联邦学习过程。我们提出了几个开放的研究挑战及其可能的解决方案。最后，我们对未来的研究进行了展望。
Recent years have witnessed a rapid proliferation of smart Internet of Things (IoT) devices. IoT devices with intelligence require the use of effective machine learning paradigms. Federated learning can be a promising solution for enabling IoT-based smart applications. In this article, we present the primary design aspects for enabling federated learning at the network edge. We model the incentive-based interaction between a global server and participating devices for federated learning via a Stackelberg game to motivate the participation of the devices in the federated learning process. We present several open research challenges with their possible solutions. Finally, we provide an outlook on future research.