Device monitoring services have increased in popularity with the evolution of recent technology and the continuously increased number of Internet of Things (IoT) devices. Among the popular services are the ones that use device location information. However, these services run into privacy issues due to the nature of data collection and transmission. In this work, we introduce a platform incorporating Federated Kalman Filter (FKF) with a federated learning approach and private blockchain technology for privacy preservation. We analyze the accuracy of the proposed design against a standard Kalman Filter (KF) implementation of localization based on the Received Signal Strength Indicator (RSSI). The experimental results reveal significant potential for improved data estimation for RSSI-based localization in device monitoring.

翻译：设备监控服务随着近年技术的演进以及物联网设备数量的持续增长而日益普及。其中，利用设备位置信息的服务尤为常见。然而，由于数据采集与传输的特性，这些服务面临着隐私问题。本文提出一个整合了联邦卡尔曼滤波器、联邦学习方法以及私有区块链技术的平台，旨在实现隐私保护。我们针对基于接收信号强度指示器的定位场景，将所提出的设计方案与标准卡尔曼滤波器的实现进行了精度对比分析。实验结果表明，在设备监控中基于RSSI的定位任务中，该方法在数据估计性能方面展现出显著的提升潜力。