Mitigating Security and Privacy Challenges in Wireless Sensor Networks Using Federated Learning Approach

By concentrating sensitive information in one location, traditional centralized data processing increases the vulnerabilities of Wireless Sensor Networks (WSNs) to data interception, tampering, unauthorized access, and privacy issues when transmitting sensitive data. Implementing a decentralized machine learning approach would benefit efforts at mitigating these vulnerabilities. In this study, the federated learning approach—a decentralized machine learning approach—was used to address security and privacy challenges in WSNs. The method adopted involved the initialization of sensor data and model parameters, letting the system enter a federated learning loop for each device to update its model using its data, aggregating the local updates into the global model, and using the model for prediction. The algorithm was implemented using MATLAB. Results showed that federated learning improved the resilience of WSNs, achieving 60% reliability and a mean square error of 1.46. This indicates that federated learning can handle the security and privacy concerns in WSNs effectively by decentralizing data processing and preserving privacy. Its ability to protect sensitive information while ensuring the accuracy of data analysis makes it a valuable approach for advancing sensor network technologies across various fields.