Battery-Aware Dynamic Adaptive Low-Power Device Selection for IoT-Enabled FL Networks
The integration of Internet of Things (IoT) and Federated Learning (FL) marks a significant step towards pervasiveness, supported by distributed computational resources and enhanced by 5G advancements. However, the efficient and sustainable operation of IoT-enabled FL systems faces critical challenges, particularly in selecting devices that balance energy consumption and system performance. To address this, we propose the Battery-Aware Dynamic and Automated Device Selection (DADS) framework, a novel solution specifically designed for IoT-enabled FL environments. DADS introduces an innovative adaptive mechanism that dynamically adjusts optimization parameters, such as inertia weights and crossover/mutation rates, ensuring a seamless balance between exploration and exploitation. Unlike conventional optimization approaches, DADS employs uniquely developed Adaptive Particle Swarm Optimization (APSO) and Adaptive Genetic Algorithm (AGA) within a cohesive framework. This design enables DADS to respond to dynamic IoT network conditions, such as fluctuating battery levels and device capabilities, ensuring energy-efficient device selection while preserving robust performance. Through extensive performance analysis, DADS demonstrates its novelty by achieving a 20% reduction in energy consumption and a 30% improvement in FL training time compared to state-of-the-art methods. Moreover, DADS significantly enhances battery lifespan, reducing degradation by more than 50%, and optimizes communication efficiency, extending the operational sustainability of IoT devices. These results position DADS as a groundbreaking framework, setting a new benchmark for energy-aware and sustainable IoT-enabled FL systems.