Management Beyond the Edge
Abstract
This research presents Carbon-Conscious Federated Reinforcement Learning (CCFRL), an innovative multi-objective orchestration framework designed to address the urgent need for sustainability in federated learning (FL). By leveraging reinforcement learning (RL), CCFRL dynamically optimizes client allocation and resource utilization to simultaneously enhance model performance and reduce carbon emissions in real time. The framework achieves a delicate balance between high-quality machine learning and carbon-efficient operation, even in scenarios involving heterogeneous, non-IID, and large-scale datasets.
While conventional distributed learning approaches often prioritize energy reduction within centralized data centers, they tend to overlook the broader carbon footprint associated with decentralized systems. Existing static and greedy strategies, though focused on short-term carbon constraints, frequently compromise performance by excluding energy-intensive but high-quality clients. In contrast, CCFRL introduces advanced state representations—such as Dirichlet distribution and Kullback-Leibler (KL) divergence—to better manage data heterogeneity and preserve model accuracy across diverse client landscapes.
A key milestone of this research has been the peer-reviewed publication of the CCFRL framework in the IEEE Internet of Things Journal (2024), validating its foundational methodology and experimental impact. Empirical evaluations demonstrate that CCFRL significantly improves energy efficiency by up to 61.78% and reduces carbon emissions by 64.23%, all while maintaining or even enhancing model accuracy. Furthermore, its context-aware knowledge integration enables up to 13% improvement in maximum achievable accuracy and up to 73% reduction in resource consumption.
This work also provides a comprehensive assessment of the carbon footprint in distributed learning by benchmarking CCFRL against traditional centralized methods and various decentralized techniques, including knowledge distillation, meta-learning, and transfer learning. The framework has been rigorously tested in complex environments using the Berzelius high-performance computing system, ensuring reproducibility and scalability across diverse learning tasks.
Looking ahead, this research aims to expand the reach of CCFRL into domains such as cloud robotics, edge AI, and mission-critical systems where sustainable intelligence is essential. By incorporating dynamic client selection, environmental-awareness, and advanced resource orchestration, CCFRL lays the groundwork for a paradigm shift in AI—where carbon consciousness is embedded into the core of learning systems.
In conclusion, CCFRL is not only a response to the immediate challenges of carbon-efficient distributed learning, but a foundational step toward establishing sustainability as a first-class objective in future AI infrastructure. This research paves the way for next-generation federated systems that are both high-performing and environmentally responsible, setting a new standard for scalable, sustainable machine learning.
