A Literature Review of Federated Learning for Energy Load Prediction in Smart Grid

Energy load prediction that incorporates federated learning has become a game-changing method for improving smart grid operations. There are issues with data privacy, computational efficiency, and scalability with traditional centralized machine learning models. This study examines the use of federated learning to forecast energy loads in smart grids, reviewing the existing literature and drawing conclusions about its benefits, drawbacks, and potential future applications. We take a look at how well different federated learning algorithms—like FLchain, FedAvg, FedSGD, and FedNorm—handle decentralized data while keeping model correctness in mind. Furthermore, the potential of deep learning methods like Temporal Convolutional Networks (TCN), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM) to enhance predictive capabilities is investigated. The article also mentions how edge computing is being used to improve federated learning by minimizing latency and making the most of available resources. Still, problems like data heterogeneity, communication overhead, and model convergence are there, even with all the progress. Improving privacy-preserving methods, federated learning frameworks, and model flexibility to changing energy usage patterns should be the focus of future study. Smart grid energy management that is efficient, secure, and scalable can be achieved through the use of federated learning, which leverages decentralized intelligence and modern machine learning approaches.