Energy-Efficient Quantized Federated Learning for Resource-constrained IoT devices
This work addresses energy efficiency and reliability issues for federated learning in resource-constrained IoT deployments, representing an incremental improvement over standard FL methods.
The paper tackled the problem of high energy consumption and unreliable communication in federated learning for IoT devices by proposing a framework integrating finite blocklength transmission, model quantization, and error-aware aggregation, resulting in up to 75% energy reduction while maintaining robust model accuracy.
Federated Learning (FL) has emerged as a promising paradigm for enabling collaborative machine learning while preserving 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 uplink 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. Index Terms: Federated learning, IoT, finite blocklength, quantization, energy efficiency.