QFL-ADEC

QFL-ADEC

Quantum Federated Learning for Anomaly Detection in Electrical Consumption

Advancing Energy Monitoring with Quantum Federated Learning for Secure and Efficient Anomaly Detection.

About the Project

1. Project Overview

Development of a Quantum Federated Learning (QFL) infrastructure for training AI models.
Utilizes electricity consumption data from multiple energy companies.
Aims to enhance Anomaly Detection for:
- Identifying unauthorized operations
- Detecting measurement defects
- Mitigating energy losses
- Optimizing energy distribution and blackout management

2. Research & Technological Advancements

Focus on Quantum Machine Learning (QML) techniques for:
- Advanced data analysis
- Proactive anomaly detection
Federated learning ensures data privacy:
- Prevents direct sharing of sensitive energy consumption data
- Allows organizations to train highly customized AI models

3. Operational Implementation

Development of a dedicated dashboard for real-time monitoring.
Compliant with AI Act regulations to ensure ethical AI deployment.
Integrates a Human-In-The-Loop (HITL) approach:
- Operators actively monitor and manage the learning process
- Ensures AI decision-making remains supervised and transparent
All operations securely recorded on a blockchain for accountability and traceability.

Project Impact

1. Enhanced Energy Infrastructure Resilience & Efficiency

Short-term benefits:
- Enables rapid anomaly detection and response
- Minimizes blackout risks and service disruptions
Long-term impact:
- Optimizes energy distribution and resource allocation
- Improves service quality and grid stability
- Reduces energy waste and enhances sustainability

2. Advanced Tools for Technical Staff

Access to cutting-edge analytics for:
- Identifying consumption patterns
- Detecting irregularities and inefficiencies
- Refining management strategies with data-driven insights
Supports targeted preventive policies, reducing:
- Network disruptions
- Unauthorized energy usage

3. Economic Growth & Industry Leadership

Competitive advantages:
- Adoption of AI & Quantum Computing (QC) drives industry leadership
- Organizations attract strategic investments and partnerships
Job creation & innovation:
- Generates specialized roles in AI, QC, and energy tech
- Strengthens sector-wide innovation and global competitiveness

4. Cost Savings & Environmental Impact

Operational savings:
- Reduced downtime and optimized maintenance
- Improved asset utilization for lower operational costs
Sustainability contributions:
- Decreased energy waste
- Lower carbon footprint, aligning with global decarbonization goals

5. Robust Data Security & Compliance

Quantum Federated Learning (QFL):
- Ensures only AI model weights are shared, protecting user data
- Maintains full GDPR compliance
Additional security measures:
- Blockchain accountability for transparency and trust
- Human-in-the-Loop (HITL) paradigm to enhance system reliability

6. Research, Education & Scientific Advancement

Fosters collaboration with research institutions and universities
Drives advancements in AI & QC for energy management
Supports continuous learning and innovation within the field

Project Details

Duration: 12 months
Project Acronym: QFL-ADEC
Project Title: Quantum Federated Learning for Anomaly Detection in Electrical Consumption
Partners:
- I.T. Svil Srl (SME) – Gianvito Petrillo (Gianvito.petrillo@itsvil.it)
Funding: NQSTI, PNRR MISSIONE 4, FINANZIATO DALL’UNIONE EUROPEA – NEXTGENERATIONEU