AI training adjusts to renewable energy limits using game theory
A Game-Theoretic Framework for Incentive-Compatible AI training Under Renewable-Energy Constraints
Emerging TechnologiesMachine Learning
Summary
Training AI can use a lot of electricity, and some of that comes from renewable energy like solar or wind, which isn't always available. The authors created a way for multiple AI training systems to decide how much to work based on how much green energy is around and the costs of using other energy. They use game theory to model how these systems make smart choices to avoid using polluting energy while still training well. Their experiments show this approach can stop the use of non-renewable grid power without hurting AI learning.
What this means in practice
- •For cloud infrastructure teams: Coordinate distributed AI training workloads to minimize fossil-fuel energy use while maintaining training quality.
- •For smart grid operators: Incorporate flexible AI training demand to balance renewable energy availability and grid load more efficiently.
Authors
Konstantinos Varsos, Ramin Khalili, Adamantia Stamou, George D. Stamoulis, Vasillios A. Siris
Abstract
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.