Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning

A recent study highlights the importance of electric load forecasting for maintaining stability in smart grids, emphasizing the role of smart meters in collecting household energy data. Traditional machine learning methods often compromise data privacy due to the need for data sharing. However, the introduction of federated learning offers a promising solution by allowing distributed machine learning models to operate locally at smart meters, thus enhancing privacy while improving forecasting accuracy. This advancement is crucial for the future of energy management and could lead to more efficient and secure smart grid systems.