High-Performance Federated Architectures for Time-Series Forecasting in Next-Generation Smart Grids

Accurate energy forecasting is critical to the management of smart grid operations, including demand response, integration of renewable energy, and real-time operational stability. Traditional centralized machine learning methods are accurate, but they have problems with data heterogeneity across consumers and regions, communication bottlenecks, and privacy. A high-performance federated learning (FL) architecture for time-series forecasting in next-generation smart grids is presented in this paper. It makes use of distributed computing across households, feeders, and regional nodes and makes use of privacy-preserving techniques like Secure Multiparty Computation (SMPC) and Differential Privacy (DP). FL is a feasible strategy for scalable, secure, and private smart grid operations because experimental results on benchmark datasets demonstrate that it can achieve 97-98% of the centralized model accuracy, reduce communication costs by 40-60%, and offer strong privacy guarantees.