15 Topics
10 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
- AI Researchers and Research Scientists
- Privacy-Preserving AI Specialists
- Cybersecurity and Information Security Professionals
- Cloud and Distributed Systems Engineers
- MLOps Engineers
- Data Architects and AI Solution Architects
- Privacy and Data Protection Professionals
- AI Governance and Responsible AI Specialists
- Risk and Compliance Professionals
- Technology and Digital Transformation Managers
- Professionals developing AI applications using distributed data
- Researchers working in machine learning, privacy, or distributed computing
- Software Engineers building decentralized AI systems
- Technical leaders overseeing AI and data modernization initiatives
Session Objectives
- Understand Federated Learning Principles
- Master Local Training & Aggregation
- Ensure Data Privacy & Security
- Manage Heterogeneity (Non-IID Data)
- Optimize Communication Efficiency
- Apply Federated Learning to LLMs & Real-World Use Cases
- Build and Deploy Federated Systems
About the Course
This advanced training course provides participants with a practical understanding of Federated Learning (FL) and its role in developing decentralized, secure, and privacy-preserving artificial intelligence systems. Participants will explore how machine learning models can be trained across distributed devices, organizations, or data environments without requiring sensitive raw data to be centrally collected.
The course examines federated learning architectures, distributed model training, client-server coordination, aggregation techniques, privacy-enhancing technologies, communication efficiency, security threats, and practical implementation challenges. Participants will explore approaches such as Federated Averaging (FedAvg), differential privacy, secure aggregation, encryption, and techniques for managing heterogeneous and non-IID datasets.
Through practical exercises and real-world use cases, participants will learn how federated learning can be applied in sectors such as financial services, healthcare, telecommunications, smart devices, government, and cybersecurity. The course also addresses model poisoning, inference attacks, data leakage risks, governance, compliance, and responsible deployment of privacy-preserving AI solutions.
Curriculum & Topics
15 Topics | 75 Sessions
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Workshop 1.1:
Fundamentals of federated learning.
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Workshop 1.2:
Overview of secure aggregation, model averaging, and client-server architectures.
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Workshop 1.3:
Setting up a federated learning development environment.
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Workshop 1.4:
Introduction to federated learning frameworks and tools.
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Workshop 1.5:
Best practices for federated learning.
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Workshop 2.1:
Implementing secure aggregation techniques for privacy-preserving model training.
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Workshop 2.2:
Utilizing homomorphic encryption and secure multi-party computation.
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Workshop 2.3:
Designing and building secure aggregation protocols.
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Workshop 2.4:
Optimizing secure aggregation for communication efficiency.
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Workshop 2.5:
Best practices for secure aggregation.
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Workshop 3.1:
Implementing model averaging and aggregation strategies.
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Workshop 3.2:
Utilizing weighted averaging and federated averaging algorithms.
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Workshop 3.3:
Designing and building model aggregation pipelines.
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Workshop 3.4:
Optimizing aggregation for model convergence and accuracy.
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Workshop 3.5:
Best practices for model aggregation.
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Workshop 4.1:
Implementing client-server architectures for federated learning systems.
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Workshop 4.2:
Utilizing distributed training frameworks (Flower, TensorFlow Federated).
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Workshop 4.3:
Designing and building scalable federated learning architectures.
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Workshop 4.4:
Optimizing client-server communication and coordination.
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Workshop 4.5:
Best practices for client-server architectures.
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Workshop 5.1:
Designing and building federated learning models for distributed data.
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Workshop 5.2:
Implementing model partitioning and distributed training strategies.
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Workshop 5.3:
Utilizing model adaptation techniques for heterogeneous data.
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Workshop 5.4:
Optimizing models for federated learning environments.
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Workshop 5.5:
Best practices for model design.
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Workshop 6.1:
Optimizing federated learning for communication efficiency and scalability.
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Workshop 6.2:
Utilizing model compression and sparsification techniques.
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Workshop 6.3:
Implementing asynchronous and hierarchical federated learning.
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Workshop 6.4:
Designing scalable federated learning systems.
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Workshop 6.5:
Best practices for communication efficiency.
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Workshop 7.1:
Debugging common challenges in federated learning.
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Workshop 7.2:
Analyzing model convergence and communication issues.
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Workshop 7.3:
Utilizing troubleshooting techniques for problem resolution.
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Workshop 7.4:
Resolving common federated learning challenges.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing privacy-preserving techniques in federated learning.
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Workshop 8.2:
Utilizing differential privacy and local differential privacy.
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Workshop 8.3:
Designing and building privacy-preserving federated learning systems.
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Workshop 8.4:
Optimizing privacy-preserving mechanisms for accuracy.
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Workshop 8.5:
Best practices for privacy.
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Workshop 9.1:
Integrating federated learning with real-world applications and datasets.
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Workshop 9.2:
Utilizing APIs and data connectors.
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Workshop 9.3:
Implementing federated learning in healthcare and finance.
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Workshop 9.4:
Optimizing integration for specific application domains.
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Workshop 9.5:
Best practices for integration.
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Workshop 10.1:
Understanding how to handle data heterogeneity and non-IID data in federated learning.
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Workshop 10.2:
Utilizing data sampling and weighting techniques.
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Workshop 10.3:
Designing and building robust federated learning algorithms.
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Workshop 10.4:
Optimizing models for non-IID data distributions.
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Workshop 10.5:
Best practices for data heterogeneity.
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Workshop 11.1:
Exploring advanced federated learning techniques (differential privacy, split learning).
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Workshop 11.2:
Utilizing differential privacy for rigorous privacy guarantees.
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Workshop 11.3:
Implementing split learning for collaborative model training.
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Workshop 11.4:
Designing and building advanced federated learning systems.
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Workshop 11.5:
Optimizing advanced techniques for specific applications.
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Workshop 11.6:
Best practices for advanced techniques.
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Workshop 12.1:
Implementing federated learning for mobile device applications.
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Workshop 12.2:
Utilizing federated learning for healthcare data analysis.
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Workshop 12.3:
Implementing federated learning for financial risk modeling.
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Workshop 12.4:
Utilizing federated learning for IoT data processing.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing TensorFlow Federated and Flower for federated learning.
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Workshop 13.2:
Implementing federated learning algorithms with frameworks.
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Workshop 13.3:
Designing and building federated learning pipelines.
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Workshop 13.4:
Optimizing tool usage for efficient development.
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Workshop 13.5:
Best practices for framework implementation.
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Workshop 14.1:
Implementing model evaluation and monitoring for federated learning.
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Workshop 14.2:
Utilizing metrics for model accuracy and privacy preservation.
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Workshop 14.3:
Designing and building monitoring dashboards.
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Workshop 14.4:
Optimizing monitoring for real-time insights.
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Workshop 14.5:
Best practices for monitoring.
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Workshop 15.1:
Emerging trends in federated learning.
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Workshop 15.2:
Utilizing AI for automated federated learning.
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Workshop 15.3:
Implementing federated learning in edge computing environments.
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Workshop 15.4:
Best practices for future applications.
