mlsanityData to 14 Oct 2026

A Game-Theoretic Framework for Incentive-Compatible AI training Under Renewable-Energy Constraints

arXiv 2609.15389 ↗First posted 14 Sep 2026cs.ET cs.LGcited by 0

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Abstract (arXiv)

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.

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References · 20

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2020Scaling Laws for Neural Language Modelscited by 6,586
  3. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
  4. 2019Towards Federated Learning at Scale: System Designcited by 832
  5. 2021Carbon Emissions and Large Neural Network Trainingcited by 567
  6. 2019Quantifying the Carbon Emissions of Machine Learningcited by 521
  7. 2019Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanismcited by 75
  8. 2020Collaborative Machine Learning with Incentive-Aware Model Rewardscited by 71
  9. 2020Can Federated Learning Save The Planet?cited by 52
  10. 2019Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approachcited by 45
  11. 2019Motivating Workers in Federated Learning: A Stackelberg Game Perspectivecited by 45
  12. 2021Carbon-Aware Computing for Datacenterscited by 41
8 more
  1. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  2. 2020An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approachcited by 39
  3. 2023A Survey for Federated Learning Evaluations: Goals and Measurescited by 16
  4. 2024Green Federated Learning: A new era of Green Aware AIcited by 12
  5. 2023FedZero: Leveraging Renewable Excess Energy in Federated Learningcited by 11
  6. 2024Federated Learning and Free-riding in a Competitive Marketcited by 2
  7. 2026Optimal Energy-Aware Service Management in Future Networks with a Gamified Incentives Mechanismcited by 1
  8. 2026GreenFLag: A Green Agentic Approach for Energy-Efficient Federated Learningcited by 1

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