Federated Learning over WSN for Privacy-Preserving IoT Data Analysis
Many Internets of Things (IoT) applications are built on the backbone of a Wireless Sensor Network known as WSNs which are used to collect large amounts of data used in smart cities, healthcare, industrial monitoring, and environmental sensing. Nevertheless, the conventional centralized data analysis systems demand transmission of raw sensor data to the cloud servers, which presents grave issues associated with privacy of data, security, heavy data communication, and energy usage. The concept of Federated Learning (FL) has become one of the most prospective ways to overcome these challenges as it allows collaborative training of models at distributed sensor nodes without exchanging raw data. The paper explores the use of federated learning in place of WSNs in the privacy analysis of IoT data. The suggested framework enables sensor nodes to locally develop machine learning models based on their individual data and transmit only updates to the models to a coordinating server or aggregator. The framework saves sensitive data in the source and thereby provides high protection to privacy and also minimizes the cost of communication. To meet the mobile nature of WSN nodes, the model uses lightweight learning algorithms, energy-sensitive update scheduling, and secure aggregation to build on the resource limitations of a small network node. Performance analysis proves that a federated learning system can reach similar levels of accuracy as those of centralized learning and significantly enhance the degree of privacy of data, decrease the volume of network traffic and increase the lifespan of a network. The findings indicate the viability and efficacy of federated learning as a scalable and secure system of distributed intelligence in WSN-based IoT systems. The current study offers important information in the development of future-generation privacy-sensitive IoT analytics.