Federated LoRA fine-tuning of a pre-trained time-series model on Indian market price data overfits, but differential privacy noise at epsilon=5 acts as regularization and cuts mean absolute percentage error by 31% relative to zero-shot.
AI-based market intelligence systems for farmer collectives: A case study from India,
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FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting
Federated LoRA fine-tuning of a pre-trained time-series model on Indian market price data overfits, but differential privacy noise at epsilon=5 acts as regularization and cuts mean absolute percentage error by 31% relative to zero-shot.