Hardware-algorithm co-design of Energy Efficient Federated Learning in Quantized Neural Network

Abstract

The real-world implementation of federated learning (FL) for smart device-based applications necessitates energy-efficient convergent models due to resource-constrained devices. In the literature, deep learning (DL)–based models are frequently trained on FL to obtain high accuracy and quick convergence employing a 32-bit floating point precision level. However, such circumstances are not feasible for devices with limited resources. DL models demand a significant amount of processing power and energy, which has a noticeable negative impact on the environment. Hence, it is necessary to reduce the level of precision in DL models to enhance energy efficiency. In this paper, we propose a low-precision level green federated learning (GFL) model to maintain the balance of communication cycles, energy efficiency, and accuracy with an admissible convergence rate of the deep learning algorithms. Our proposed GFL framework achieves 98.04% server accuracy and 97.69% federated accuracy with a faster convergence rate and fewer communication rounds than state-of-the-art methods on the UCI human activity recognition dataset.