The QFL-ADEC Project

The Quantum Federated Learning for Anomaly Detection in Energy Consumption (QFL-ADEC) project aims to develop a decentralized AI infrastructure for detecting anomalies in electricity consumption. By integrating Quantum Computing (QC) and Federated Learning (FL), the system enhances detection speed, accuracy, and data privacy.

Context and State of the Art

Energy grid inefficiencies, blackouts, and anomalies create economic and environmental challenges. Despite advancements in smart grid technologies, real-time anomaly detection remains a critical issue.

Current Technologies & Limitations

• IoT & Smart Sensors: Used for energy consumption data collection.
• Machine Learning (ML): Enables automated pattern recognition but struggles with large-scale data analysis.
• Quantum Machine Learning (QML): Leverages quantum mechanics principles to solve complex problems beyond classical computing capabilities.

Quantum-Based Anomaly Detection

• Quantum Variational Rewinding (QVR) Algorithm: Detects anomalies in energy consumption time-series data.
• Quantum Neural Networks (QNNs): Convert traditional data into quantum states for efficient processing.

Federated Learning for Privacy-Preserving AI

The project employs Quantum Federated Learning (QFL), where AI models are trained locally, sharing only model weights with a central server to maintain data privacy.

• Global quantum model distribution to local nodes.
• Local training using company-specific data.
• Model weight aggregation at a central server.
• Iterative refinement for continuous performance improvement.

Project Goals & Expected Outcomes

Key Objectives

• QFL Infrastructure: Secure collaboration between energy companies.
• Enhanced Anomaly Detection: Quantum-driven AI models for faster, more accurate detection.
• Energy Efficiency & Sustainability: Reduces waste and blackout risks.
• Legal & Ethical Compliance: GDPR adherence, HITL oversight, and blockchain-based accountability.

Innovation Highlights

• Quantum Computing: Enables large-scale data processing at unprecedented speeds.
• Federated Learning: Each organization customizes AI models while preserving privacy.
• Regulatory Compliance: AI Act & GDPR alignment through HITL and blockchain tracking.
• Environmental Impact Reduction: Optimized energy use and reduced waste.

This project integrates QC, AI, and federated learning to create a scalable, secure, and high-performance anomaly detection system, driving innovation in energy management.