The aim of Quantum Federated Learning for Anomaly Detection in Electrical Consumption (QFL-ADEC) is the development of a Quantum Federated Learning (QFL) infrastructure to train AI models on electricity consumption data from multiple energy providers, enhancing anomaly detection to identify unauthorized activity, measurement defects, and energy inefficiencies. The approach improves energy distribution, blackout management, and overall grid resilience.
Key innovations include the use of Quantum Machine Learning (QML) for advanced analysis and federated learning to ensure data privacy. A real-time dashboard, compliance with the AI Act, and a Human-in-the-Loop (HITL) framework ensure ethical and transparent AI deployment, with all actions traceable via blockchain.
Project Website: https://qfl-adec.github.io/