Federated Learning on Raspberry Pi 4: A Comprehensive Power Consumption Analysis

Edge Computing, a rapidly evolving sector within information technology, redefines data processing and analysis by shifting it closer to the data source, away from centralized cloud servers. This paradigm promises substantial benefits for diverse applications. In the realm of Artificial Intelligence and Machine Learning, Federated Learning emerges as a pioneering technique that harnesses Edge Computing for statistical model training. Federated Learning presents numerous advantages over traditional centralized Machine Learning, including reduced latency, heightened privacy, and real-time data processing. Nonetheless, it introduces concerns regarding energy consumption, particularly for battery-powered Edge devices designed for remote or harsh environments. This study provides a comprehensive assessment of power consumption within the context of Federated Learning operations. To achieve this, a Raspberry Pi 4 and an INA 219 current sensor are employed. Results show that, during communication operations, the power consumption of the target device increases from a minimum of 8% to a maximum of 32% with respect to its idle state. During the local training operations it increases respectively by up to 32% for a CNN model and by up to 40% for a RNN model.