Energy-Efficient Quantized Federated Learning for Resource-constrained IoT devices
Wilfrid Sougrinoma Compaoré, Yaya Etiabi, El Mehdi Amhoud, Mohamad Assaad · 2025
Federated Learning (FL) has emerged as a promising paradigm for enabling collaborative machine learning while pre-serving data privacy, making it particularly suitable for Internet of Things (IoT) environments. However, resource-constrained IoT devices face significant challenges due to limited energy, unreliable communication channels, and the impracticality of assuming infinite blocklength transmission. This paper proposes a federated learning framework for IoT networks that integrates finite blocklength transmission, model quantization, and an error-aware aggregation mechanism to enhance energy efficiency and communication reliability. The framework also optimizes up-link transmission power to balance energy savings and model performance. Simulation results demonstrate that the proposed approach significantly reduces energy consumption by up to 75% compared to a standard FL model, while maintaining robust model accuracy, making it a viable solution for FL in real-world IoT scenarios with constrained resources. This work paves the way for efficient and reliable FL implementations in practical IoT deployments.