The need for scalable, effective, and secure data processing frameworks has increased due to the Internet of Things’ (IoT) explosive growth. Federated Learning (FL) and Federated Averaging (FA) have shown great promise in enabling shared intelligence while maintaining privacy in the Federated Virtual Sensors for IoT (FVSI) framework. The adaptation of FL in complex IoT ecosystems is restricted by issues such as the opacity of federated models and the resource limitations on IoT devices. To overcome these constraints, this work expands the FVSI framework using adaptive offloading techniques. The improved FVSI framework uses an adaptive offloading technique to distribute computational workloads between edge servers and IoT devices according to network conditions and resource availability. For devices with limited resources, this guarantees efficient resource usage and reduces latency. The framework’s capacity to sustain consistent performance as the number of devices and tasks increases is demonstrated by extensive scalability testing scenarios. Important indicators like task success rates, latency reduction, and energy efficiency show how resilient the suggested strategy is to different setups. By combining FL with an intelligent offloading strategy that preserves privacy, the proposed FVSI framework provides a scalable and reliable solution for large-scale IoT applications. This work advances the state-of-the art by addressing critical challenges in resource optimization and performance scalability, offering a practical pathway for deploying efficient and secure IoT systems in diverse real-world scenarios.