The necessity for robust and secure data processing frameworks has been brought to light by the development of Internet of Things (IoT) devices. Federated learning (FL) has gained traction as a viable method for utilizing distributed intelligence while protecting data privacy, owing to the widespread use of IoT devices. In our previous study, we developed a method that leverages federated averaging (FA) and distributed intelligence by creating a Federated Virtual Sensors for IoT (FVSI) platform to facilitate remote data processing while maintaining localized data. However, improving the privacy and resilience of the FVSI architecture has become essential as IoT systems become more vulnerable to hostile manipulations and privacy intrusions. This paper delves into the security vulnerabilities inherent in FL systems, particularly within IoT networks. By including differential privacy (DP) strategies and creating strong defenses against adversarial assaults on FL models, this study aims to expand the FVSI framework. The project intends to prevent privacy breaches in shared data among FL models by implementing advanced DP techniques. We’ll use strategies like noise addition and privacy-preserving data aggregation to ensure privacy and data utility are balanced. Through extensive validation, we demonstrate that these enhancements maintain high model performance across various IoT scenarios while preventing data breaches. Our results illustrate notable gains in FVSI’s resilience, paving the way for secure, trustworthy IoT deployments.