Near-sensor data analytics advocates processing data locally near their sources, rather than gathering them for centralized processing. It can reduce communication costs and is particularly suitable for networked sensor systems whose data are geo-distributed. As most sensors and associated devices have limited computing power, it is desirable for them to collaborate, especially in a decentralized manner, so that the workload can be distributed and no single point becomes a bottleneck. In this letter, we present a decentralized machine learning algorithm with communication compression capability that can serve as the core of a near-sensor data analytics task. Owing to its online nature and reduced communication overhead, the proposed method is particularly suitable for real-world sensor network systems with energy and bandwidth constraints.

中文翻译：

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通过压缩通信实现近传感器数据分析的去中心化在线学习


近传感器数据分析主张在数据源附近本地处理数据，而不是收集数据进行集中处理。它可以降低通信成本，特别适合数据地理分布的网络传感器系统。由于大多数传感器和相关设备的计算能力有限，因此需要它们进行协作，尤其是以去中心化的方式进行协作，以便可以分散工作负载，并且不会出现单点成为瓶颈的情况。在这封信中，我们提出了一种具有通信压缩功能的去中心化机器学习算法，可以作为近传感器数据分析任务的核心。由于其在线性质和减少的通信开销，所提出的方法特别适合具有能量和带宽限制的现实世界传感器网络系统。