Optimizing IoT data collection through federated learning and periodic scheduling

Abstract

The Internet of Things (IoT) describes a system of interlinked devices, sensors, and intelligent systems that facilitate intricate management in smart homes, industries, and cities. The devices constantly gather basic information like temperature, humidity, geographical location, and energy consumption to facilitate analytics and decision-making. However, traditional data collection methods, such as direct information transfer to a central server, face significant challenges regarding bandwidth use, energy efficiency, data security, reliability, and overall performance. These methods require robust communication infrastructures, often leading to network resource overexploitation due to raw data transmission. Although edge computing, fog computing, fedHGL, and centralized learning methods are considered modern techniques offering some advantages, they still require complex infrastructures and have the same difficulties processing heterogeneous or big datasets. Periodic scheduling is a new paradigm for federated learning, where the data will be processed locally, and only the updated model weights will be transferred to the central server. This approach significantly reduces bandwidth and energy consumption and facilitates faster model updates, enhancing the overall performance of IoT networks. Simulation results demonstrate that our proposed federated learning approach outperforms the other considered approaches on both MNIST and RT-IoT2022 datasets. It achieves on MNIST an accuracy improvement of 12 %, a reduction in convergence time of 22 %, and a bandwidth usage reduction of 21 %; and on RT-IoT2022, an accuracy enhancement of 9 %, a convergence time reduction of 18 %, and a bandwidth usage reduction of 25 %, confirming its overall superiority for IoT systems.