Federated Learning Based Privacy-Preserving Renewable Energy Consumption for the Local Community

With the growing demand for sustainable energy, there is an increasing need for efficient management and distribution of renewable energy resources. However, collecting and sharing energy consumption data in a centralized manner poses a significant privacy risk for individuals. To address this issue, Federated Learning can be leveraged to train a predictive model on decentralized data sources while preserving data privacy. In this approach, each participant can train a local model using their own energy consumption data and share the model updates with a central server. The central server then aggregates the updates to generate a global model, which can be used to predict energy consumption patterns for the local community. The proposed approach can enable effective management of renewable energy resources while preserving the privacy of individual energy consumption data. It has the potential to provide a sustainable solution to the growing demand for renewable energy consumption in local communities.