Management Beyond the Edge
Abstract
This research introduces Carbon-Conscious Federated Reinforcement Learning (CCFRL), a novel multi-objective optimal orchestration framework that addresses the need for sustainability in federated learning (FL). Leveraging reinforcement learning (RL), CCFRL dynamically optimizes client allocation and resource usage, enhancing carbon efficiency and model performance in real-time. This dual-objective approach aims to reduce carbon emissions and resource costs while maintaining machine learning quality, even with heterogeneous, non-IID, and large-scale datasets.
Traditional distributed learning methods often overlook the carbon footprint of decentralized systems, focusing primarily on energy reduction within centralized data centers. Existing static and greedy strategies prioritize short-term carbon constraints but degrade performance by excluding energy-intensive, high-quality clients. In contrast, CCFRL integrates advanced state representations, such as Dirichlet distribution and Kullback-Leibler (KL) divergence, to better manage data heterogeneity, mitigate performance loss, and maintain accuracy across diverse client data.
CCFRL addresses the shortcomings of existing methods by adopting sustainable strategies that balance immediate resource needs with long-term environmental goals. It optimizes client selection dynamically, ensuring that carbon emissions are minimized without sacrificing model performance. Experiments demonstrate that CCFRL significantly improves energy conservation by up to 61.78% and reduces carbon emissions by 64.23% while maintaining or enhancing model accuracy. Additionally, by incorporating context-aware techniques, CCFRL has shown performance gains with quality datasets, boosting maximum achievable accuracy by up to 13% and reducing resource consumption by up to 73%.
This work further investigates the carbon footprint of distributed learning by comparing it to traditional centralized learning and various distributed methods, including knowledge distillation, meta-learning, and transfer learning. The goal is to establish a carbon-efficient distributed learning approach that aligns with sustainability targets in cloud computing and decentralized environments.
To validate the proposed framework, the multi-objective orchestration algorithm will undergo extensive testing using various distributed learning algorithms and datasets in complex scenarios. This approach will address ML model deployment challenges, optimize resource utilization, and ensure robust performance under varying conditions. The Berzelius high-performance computing environment will support these evaluations, enabling the execution of reference distributed learning algorithms and comprehensive testing.
Further goals include expanding the application of CCFRL to cloud robotics, edge computing, and other areas where sustainable AI solutions are critical. By integrating advanced state representations, dynamic client selection, and context-aware knowledge distillation, this research aims to set a new standard for environmentally responsible federated learning, offering a path forward for sustainable machine learning operations.
In conclusion, CCFRL not only meets the immediate need for carbon-efficient distributed learning but also lays the groundwork for a broader impact on sustainable AI and cloud computing. By aligning resource management with environmental responsibility, this research paves the way for a new era of carbon-conscious machine learning, demonstrating that sustainability and high performance can go hand in hand in the future of computing.
