A federated collaborative short-term industrial load forecasting method integrating meta-learning and differentiated privacy allocation

Short-term load forecasting for industrial users is often limited by scarce local data and heterogeneous load patterns, while cross-factory collaboration raises serious privacy concerns due to production-sensitive information. This paper proposes a privacy-preserving federated forecasting framework for industrial short-term load prediction that integrates meta-learning and differentiated differential privacy allocation. The framework is built on federated learning, where factories perform local training and upload encrypted model updates for secure aggregation, ensuring data availability without data visibility. To address negative transfer and poor personalization caused by load heterogeneity, a meta-learning strategy is introduced to enable rapid adaptation to new factories or operating conditions using only a small amount of local data. In addition, a differentiated privacy-weight mechanism is designed to dynamically allocate privacy budgets based on data contribution and sensitivity, achieving a better trade-off between privacy protection and forecasting accuracy. Experiments on real-world industrial load datasets show that the proposed method reduces MAPE by 1.99 percentage points compared with independent training, and by 2.63 percentage points in new operating-condition scenarios, demonstrating its effectiveness for secure and collaborative industrial load forecasting.