Federated-Learning Hydrogen Leak-Detection Network Across Gas Utilities
Revolutionizing Gas Safety: Federated Learning for Advanced Hydrogen Leak Detection Across Utilities.
About This Course
This workshop focuses on implementing a federated learning-based hydrogen leak detection network for gas utilities. Participants will learn sensor deployment, data hygiene, and detection models, along with federated learning techniques for secure, privacy-aware leak detection. Hands-on training will cover model deployment, alert management, and integration with operational workflows to enhance safety and efficiency across utilities.
Aim
The aim of this workshop is to teach participants how to implement a federated learning-based hydrogen leak detection network across gas utilities, covering sensor deployment, data hygiene, detection models, and secure aggregation for real-time, privacy-aware leak detection and safety optimization.
Workshop Objectives
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Understand the fundamentals of hydrogen leak detection and sensor deployment.
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Learn to build and maintain clean, privacy-aware datasets for leak detection.
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Gain hands-on experience in training and deploying detection models using federated learning.
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Explore federated learning paradigms, secure aggregation, and robustness techniques.
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Integrate leak detection systems with operational workflows, including alert management and CMMS.
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Learn to monitor and track safety KPIs to optimize leak detection accuracy and response times.
Who Should Enrol?
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Professionals in gas utilities, safety management, and leak detection
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Data scientists and engineers working with machine learning and federated learning
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Researchers in environmental safety and gas infrastructure
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Engineers involved in sensor deployment, calibration, and data management
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Individuals with a basic understanding of machine learning, sensor technologies, and gas safety practices
Important Dates
Registration Ends
11/10/2025
IST 4:30 PM
Workshop Dates
11/10/2025 – 11/12/2025
IST 5:30 PM
Workshop Outcomes
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Proficiency in deploying federated learning-based hydrogen leak detection networks across gas utilities.
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Hands-on experience in sensor deployment, data hygiene, and building privacy-aware datasets.
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Ability to train and deploy detection models using federated learning techniques.
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Skills in integrating leak detection systems with operational workflows and CMMS.
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Enhanced understanding of secure aggregation, client drift monitoring, and maintaining privacy in distributed data environments.
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Ability to generate and track safety KPIs, improving overall leak detection accuracy and response times.
Fee Structure
Student Fee
₹1999 | $65
Ph.D. Scholar / Researcher Fee
₹2999 | $75
Academician / Faculty Fee
₹3999 | $85
Industry Professional Fee
₹5999 | $105
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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