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Forecasting short-term residential energy consumption is critical in modern decentralized power systems. Deep learning-based prediction methods that can handle the high variability of residential electrical loads have made models more accurate. On the other hand, these methods need a lot of sensitive information about how much people use something gathered centrally to train a forecasting model. This is not good for privacy and scalability. Moreover, models may become less accurate over time due to changing conditions. In this work, we propose a framework for energy consumption forecasting that exploits adaptive learning, federated learning, and edge computing concepts. A central server aggregates numerous long short-term memory (LSTM) models that users at various locations train with their energy consumption data to create a generalized model that uses adaptive learning to detect data drifts and …