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.

中文翻译：

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边缘计算系统中环境感知的联合区域学习

在过去的几十年中，环境污染已成为影响人们健康的主要问题。提供准确的环境传感服务具有重要意义。为了实现环境感知，分布式监测站点被用来收集全面的长期环境数据。然而，由于监测站点不足和记录不完整导致的传感数据稀少，成为细粒度环境感知的主要挑战。同时，由于网络带宽和存储空间的限制，传统的集中式训练难以满足基于大数据的任务训练需求。在本文中，我们开发了一种新颖的分布式推理框架，称为联邦区域学习（FRL），用于城市环境感知。它继承了联邦学习避免数据传输和集中存储的基本思想，同时也考虑了各个监测站点的地域特点。通过精心设计的边缘计算系统，为微云定制局部区域模型，提高推理精度。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。进行了基于两个真实世界数据集的广泛实验，以证明普遍性和有效性。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。进行了基于两个真实世界数据集的广泛实验，以证明普遍性和有效性。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。进行了基于两个真实世界数据集的广泛实验，以证明普遍性和有效性。