可再生能源约束下激励兼容的AI训练博弈论框架
A Game-Theoretic Framework for Incentive-Compatible AI training Under Renewable-Energy Constraints
- Athens University of Economics and Business（雅典经济与商业大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出博弈论框架，使AI训练智能体在可再生能源约束下策略性决策，通过激励机制消除电网能耗并保持模型性能，显著降低碳排放。
AI中文摘要：
随着人工智能系统日益依赖分布式和协作式训练，这些过程的能源足迹成为共同责任。现代AI训练通常跨越异构计算节点——从云集群到边缘设备——其能源可用性在空间和时间上都是可变的。与此同时，可再生能源电网经历着日益增长的过剩发电水平，这为将计算工作负载与低碳能源供应对齐创造了机会。在这项工作中，我们开发了一个碳感知AI训练的博弈论模型，其中自主智能体在有限的可再生能源可用性下，策略性地选择是否参与以及训练的强度。每个智能体在绿色能源预算内保持的奖励、电网消耗的惩罚以及递减的学习收益之间进行权衡。虽然我们的框架广泛适用于分布式AI训练，但由于其去中心化结构和灵活调度，我们以联邦学习作为代表性案例进行研究。我们分析了均衡的存在性、效率和自适应动力学，并提供了模拟证据表明，适当设计的激励机制可以消除基于电网的能源使用，同时保持模型性能。我们的研究结果表明，激励兼容的训练机制可以在可再生能源约束下提高能源效率并大幅减少碳排放。
英文摘要：
As artificial intelligence systems increasingly rely on distributed and collaborative training, the energy footprint of these processes becomes a shared responsibility. Modern AI training often unfolds across heterogeneous compute nodes-ranging from cloud clusters to edge devices-whose energy availability is spatially and temporally variable. At the same time, renewable energy grids experience growing levels of excess generation, creating opportunities to align computational workloads with low-carbon energy supply. In this work, we develop a game-theoretic model of carbon-aware AI training in which autonomous agents strategically choose whether to participate and how intensively to train under limited renewable energy availability. Each agent balances diminishing learning returns, rewards for remaining within green-energy budgets, and penalties for grid consumption. While our framework applies broadly to distributed AI training, we examine Federated Learning as a representative case study due to its decentralized structure and flexible scheduling. We analyze equilibrium existence, efficiency, and adaptive dynamics, and provide simulation evidence that appropriately designed incentives can eliminate grid-based energy usage while preserving model performance. Our findings demonstrate how incentive-compatible training mechanisms can enhance energy efficiency and sharply reduce carbon emissions under renewable-energy constraints.
