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Generating synthetic power-grid scenarios that are both operationally feasible and statistically accurate could revolutionize planning and resilience assessments in power systems.
Federated learning with LLMs for recommendation can be improved by sharing directional update components and learning personalized integration weights, outperforming naive aggregation.
