Evaluating Federated Learning in IoT: Advancing Scalability, Privacy, and Real-Time Intelligence

Abstract

Federated learning (FL) offers a promising approach to improving the efficiency and privacy of Internet of Things (IoT) systems, particularly in environments where data is distributed across numerous devices. However, challenges remain in selecting the right FL model for specific applications, managing data privacy, and optimizing system performance. This paper evaluates four FL models - FedAvg, FedPer, FedProx, and FedSGD - against these challenges, focusing on their ability to optimize resource management, enhance data privacy, and reduce communication costs in real-time IoT settings. We provide a detailed comparison of their loss rates, execution times, and scalability, offering valuable insights into how each model performs under varying conditions. By addressing these key issues, our work contributes to the effective deployment of FL in distributed IoT systems, guiding the selection of the most suitable model for diverse applications.