Federated Learning for System-Aware In-Device Task Scheduling in IoT Ecosystems

Modern IoT and Fog computing environments consist of numerous interconnected and often powerful devices. Many of these devices are capable of processing not only local tasks but also tasks offloaded to them. Efficient handling of offloaded tasks is essential to maintain devices performance and responsiveness. A key challenge lies in optimizing how the offloaded tasks are scheduled and executed within devices while respecting their local tasks demands for CPU, RAM, and energy. This work proposes the application of Federated Learning (FL) to enable collaborative and privacy-preserving training of task scheduling models across distributed IoT devices. Each device learns to optimize task execution locally, while contributing to a global model that benefits the entire system. By leveraging FL, devices can adapt their scheduling strategies in real time without exposing sensitive local data. The proposed approach aims to: improve offloaded task waiting time, balance resource utilization, minimize energy consumption and the overall negative impact of offloaded tasks on a device's functionality. The contribution of this research lies in exploring FL as a scalable and privacy-aware framework for optimizing in-device scheduling of offloaded tasks in heterogeneous IoT environments.