Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/1622 2019 2019 Department of Entrepreneurship and Logistics, Plekhanov Russian University of Economics, 117997, Moscow, Russia Things Irina V. .. Department of Logistics, State University of Management, 109542, Moscow, Russia Denis A. Pustokhin  The Internet of Things (IoT) has become a ubiquitous technology that enables the collection and analysis of large amounts of data. However, the limited resources of IoT devices pose challenges to enabling responsive decision-making. Many communications are required for network training, yet network updates can be very big if they include many parameters. Participants and the IoT ecosystem both bear the brunt of federated learning's high Latency due to the magnitude of its communications infrastructure requirements. In this paper, we propose a Federated Knowledge Purification (FKP) approach based on dynamic reciprocal knowledge purification and adaptive gradient compression, two strategies that allow for low-latency communication without sacrificing effectiveness, which enables responsive IoT devices with limited resources. The FKP approach leverages a collaborative learning approach to enable IoT devices to learn from each other's experiences while preserving the privacy of their data. A smaller model is trained on the aggregated knowledge of a larger model trained on a centralized server, and this smaller model can be deployed on IoT devices to enable responsive decision-making with limited computational resources. Experimental results demonstrate the effectiveness of the proposed approach in improving the performance of IoT devices while maintaining the privacy of their data. The proposed approach also outperforms existing federated learning methods in terms of communication efficiency and convergence speed. 2022 2022 71 80 10.54216/JISIoT.070207 https://www.americaspg.com/articleinfo/18/show/1622