A Game-Theoretic Framework for Incentive-Compatible AI training Under Renewable-Energy Constraints
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 ...
| Authors | Konstantinos Varsos, Ramin Khalili, Adamantia Stamou et al. |
|---|---|
| Primary category | cs.ET |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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