Federated Region-Learning for Environment Sensing in Edge Computing System
Federated Region-Learning for Environment Sensing in Edge Computing System
复制标题
边缘计算系统中环境感知的联邦区域学习
DOI：
10.1109/tnse.2020.3016035
复制
发表时间：
2020-10-01
影响因子：
6.6
通讯作者：
Ma, Huadong
中科院分区：
文献类型：
--
作者：
Gao, Yujia;Liu, Liang;Ma, Huadong
在过去的几十年里，环境污染已经成长为影响人们健康的一个主要问题。提供精准的环境传感服务具有重要意义。为了实现环境传感，使用分布式监测点收集全面的长期环境数据。然而，由于监测点不足而导致的传感数据稀疏及其记录不完整，成为细粒度环境传感的主要挑战。同时，由于网络带宽和存储空间的限制，传统的集中训练难以满足基于大数据的任务训练要求。提出了一种用于城市环境感知的分布式推理框架--联邦区域学习(FRL)。它继承了联邦学习避免数据传输和集中存储的基本思想，并考虑了每个监测点的区域特征。通过精心设计的边缘计算系统，为微云定制了局部区域模型，提高了推理精度。此外，我们开发了两种类型的全局模型聚合策略，以更好地针对不同的带宽需求。基于两个真实世界的数据集进行了大量的实验，以证明其普适性和有效性。
In the last decades, environmental pollution has grown up to be a major problem that influences people's health. Providing accurate environmental sensing services is of great significance. To realize environmental sensing, distributed monitoring sites are used to collect comprehensive long-term environmental data. However, sparse sensory data caused by insufficient monitoring sites and their incomplete records become the main challenge of fine-grained environment sensing. At the same time, due to the limitations of network bandwidth and storage space, traditional centralized training is difficult to meet the task training requirements based on big data. In this paper, we develop a novel distributed inference framework, named Federated Region-Learning (FRL) for urban environment sensing. It inherits the basic idea of federated learning avoiding transmission and centralized storage of data, and also considers the regional characteristics of each monitoring site. Through an elaborate designed edge computing system, a local regional model is customized for the micro cloud to improve the inference accuracy. Moreover, we develop two types of global model aggregation strategies to better target different bandwidth requirements. Extensive experiments based on two real-world datasets are performed to prove universality and effectiveness.