Pioneering Eco-Efficiency in Cloud Computing: The Carbon-Conscious Federated Reinforcement Learning (CCFRL) Approach

In response to the growing emphasis on sustainability in federated learning (FL), this research introduces a dynamic, dual-objective optimization framework called carbon-conscious federated reinforcement learning (CCFRL). By leveraging reinforcement learning (RL), CCFRL continuously adapts client allocation and resource usage in real time, optimizing both carbon efficiency and model performance. Unlike static or greedy methods that prioritize short-term carbon constraints, existing approaches often suffer from either degrading model performance by excluding high-quality, energy-intensive clients or failing to adequately balance carbon emissions with long-term efficiency. CCFRL addresses these limitations by taking a more sustainable method, balancing immediate resource needs with long-term sustainability, and ensuring that energy consumption and carbon emissions are minimized without compromising model quality, even with nonindependent and identically distributed (non-IID) and large-scale datasets. We overcome the shortcomings of existing methods by integrating advanced state representations, adaptive exploration and exploitation transitions, and stagnating detection using t-tests to better manage real-world data heterogeneity and complex, nonlinear datasets. Extensive experiments demonstrate that CCFRL significantly reduces both energy consumption and carbon emissions while maintaining or enhancing performance. With up to a 61.78% improvement in energy conservation and a 64.23% reduction in carbon emissions, CCFRL proves the viability of aligning resource management with sustainability goals, paving the way for a more environmentally responsible future in cloud computing.