The amount of data being produced at every epoch of second is increasing every moment. Various sensors, cameras and smart gadgets produce continuous data throughout its installation. Processing and analyzing raw data at a cloud server faces several challenges such as bandwidth, congestion, latency, privacy and security. Fog computing brings computational resources closer to IoT that addresses some of these issues. These IoT devices have low computational capability, which is insufficient to train machine learning. Mining hidden patterns and inferential rules from continuously growing data is crucial for various applications. Due to growing privacy concerns, privacy preserving machine learning is another aspect that needs to be inculcated. In this paper, we have proposed a fog enabled distributed training architecture for machine learning tasks using resources constrained devices. The proposed architecture trains machine learning model on rapidly changing data using online learning. The network is inlined with privacy preserving federated learning training. Further, the learning capability of architecture is tested on a real world IIoT use case. We trained a neural network model for human position detection in IIoT setup on rapidly changing data.

翻译：每秒产生的数据量正持续增长。各类传感器、摄像头及智能设备在其运行周期内不断生成数据。在云端服务器处理与分析原始数据面临带宽、拥塞、时延、隐私及安全性等多重挑战。雾计算将计算资源向物联网边缘迁移，可部分缓解上述问题。然而，物联网设备计算能力有限，不足以支撑机器学习训练。从持续增长的数据中挖掘隐藏模式与推理规则对各类应用至关重要。随着隐私保护需求日益凸显，隐私保护型机器学习亦需纳入考量。本文提出一种面向资源受限设备的雾计算分布式训练架构，该架构采用在线学习方式，针对快速变化数据进行机器学习模型训练。网络内嵌联邦学习机制以实现隐私保护。此外，我们在真实工业物联网用例中验证了该架构的学习能力：基于快速变化数据，在工业物联网场景下训练了用于人体位置检测的神经网络模型。