About Workshop
Aim
What Participants Will Learn
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Learn Federated Learning and privacy-preserving techniques.
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Gain hands-on experience building decentralized AI models.
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Understand security challenges in machine learning systems.
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Apply Federated Learning in real-world privacy-sensitive applications.
Structure
📅 Day 1 – Federated Learning Basics
- AI/ML primer; centralized vs. decentralized learning; why FL for privacy & security
- FL concepts: architecture, workflow, differences from traditional ML, real examples
- Challenges: data privacy, communication/resources, convergence/synchronization
- Applications: healthcare, finance, IoT
📅 Day 2 – Privacy & Security
- Privacy tools: differential privacy, homomorphic encryption, SMPC
- Techniques: FedAvg, cryptographic protections (with hands-on demos)
- Threats & mitigations: adversarial/poisoning/Byzantine failures; TEEs
- What’s next: trends and FL’s role in decentralized AI
📅 Day 3 – Build, Use Cases & Future
- Tooling & hands-on: TFF, PySyft; train/sync/evaluate FL models
- Industry use cases: healthcare, finance, edge, IoT; case studies
- Open challenges: scalability, heterogeneity, fairness/ethics
- Future directions: edge–cloud integration, new research, ethical outlook
Important Dates
Registration Ends
Workshop Dates
What You Will Gain
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In-depth understanding of Federated Learning and privacy-preserving AI techniques
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Hands-on experience implementing federated learning models
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Knowledge of privacy techniques like differential privacy and homomorphic encryption
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Practical skills in securing decentralized machine learning systems
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Insights into real-world applications in industries such as healthcare, finance, and IoT
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Awareness of current challenges and future trends in Federated Learning and AI security
Outcomes
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Proficiency in implementing Federated Learning models
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Strong understanding of privacy-preserving AI techniques
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Ability to apply secure, decentralized machine learning in real-world applications
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Skills to address privacy and security challenges in AI systems
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Knowledge of emerging trends in Federated Learning and data privacy
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Preparedness for careers in AI, data privacy, and machine learning security
Who Should Attend
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AI/ML practitioners and data scientists
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Machine learning engineers
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Researchers focused on privacy and decentralized systems
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Software developers interested in implementing federated learning models
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Industry professionals in healthcare, finance, IoT, and other privacy-sensitive sectors
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Students with a basic understanding of machine learning and programming (preferably Python)
Deliverables
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In-depth understanding of Federated Learning and privacy-preserving AI techniques
-
Hands-on experience implementing federated learning models
-
Knowledge of privacy techniques like differential privacy and homomorphic encryption
-
Practical skills in securing decentralized machine learning systems
-
Insights into real-world applications in industries such as healthcare, finance, and IoT
-
Awareness of current challenges and future trends in Federated Learning and AI security