论文标题
移动边缘计算中的联合学习:超越5G的边缘学习视角
Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G
论文作者
论文摘要
由于当今运行中大量的物联网设备中有大量感测数据,因此在此类数据上运行的集中机器学习算法会产生难以忍受的训练时间,因此无法满足延迟敏感的推理应用程序的要求。通过在网络边缘提供计算资源,移动边缘计算(MEC)已成为一种有前途的技术,能够与分布式的物联网设备合作,以促进联合学习,从而实现实时培训。但是,考虑到Edge服务器和IoT设备的大量感知数据以及有限的资源,确保训练效率和准确性的延迟敏感训练任务的挑战是一项挑战。因此,在本文中,我们设计了一个新颖的边缘计算辅助联盟学习框架,其中考虑了物联网设备和边缘服务器之间的通信约束以及各种物联网设备对训练精度的影响。一方面,我们采用机器学习方法来实时动态配置通信资源,以加速物联网设备和边缘服务器之间的交互作用,从而提高联合学习的训练效率。另一方面,由于各种IoT设备具有不同的培训数据集,这些数据集对Edge Server中派生的全局模型的准确性有不同的影响,因此IoT设备选择方案旨在提高Edge Servers资源约束下的培训准确性。进行了广泛的模拟,以证明引入的边缘计算辅助联合学习框架的性能。
Owing to the large volume of sensed data from the enormous number of IoT devices in operation today, centralized machine learning algorithms operating on such data incur an unbearable training time, and thus cannot satisfy the requirements of delay-sensitive inference applications. By provisioning computing resources at the network edge, Mobile Edge Computing (MEC) has become a promising technology capable of collaborating with distributed IoT devices to facilitate federated learning, and thus realize real-time training. However, considering the large volume of sensed data and the limited resources of both edge servers and IoT devices, it is challenging to ensure the training efficiency and accuracy of delay-sensitive training tasks. Thus, in this paper, we design a novel edge computing-assisted federated learning framework, in which the communication constraints between IoT devices and edge servers and the effect of various IoT devices on the training accuracy are taken into account. On one hand, we employ machine learning methods to dynamically configure the communication resources in real-time to accelerate the interactions between IoT devices and edge servers, thus improving the training efficiency of federated learning. On the other hand, as various IoT devices have different training datasets which have varying influence on the accuracy of the global model derived at the edge server, an IoT device selection scheme is designed to improve the training accuracy under the resource constraints at edge servers. Extensive simulations have been conducted to demonstrate the performance of the introduced edge computing-assisted federated learning framework.