Federated Learning Training Course: Decentralized Ml Model Training

Introduction

Unlock the potential of distributed data with our Federated Learning Training Course. This program is designed to equip you with the essential skills to train machine learning models on decentralized data, enabling you to build privacy-preserving and scalable AI solutions. In today's data-sensitive world, mastering federated learning is crucial for leveraging distributed datasets without compromising user privacy. Our federated learning training course offers hands-on experience and expert guidance, empowering you to implement cutting-edge distributed machine learning techniques.

This decentralized ML model training delves into the core concepts of federated learning, covering topics such as secure aggregation, model averaging, and client-server architectures. You'll gain expertise in using industry-standard tools and techniques to train machine learning models on decentralized data, meeting the demands of modern privacy-focused AI projects. Whether you're a machine learning engineer, data scientist, or AI researcher, this Federated Learning course will empower you to build and deploy robust federated learning systems.

Target Audience:

• Machine Learning Engineers
• Data Scientists
• AI Researchers
• Data Privacy Specialists
• Security Engineers
• Software Developers
• Anyone needing federated learning skills

Course Objectives:

• Understand the fundamentals of federated learning.
• Master secure aggregation techniques for privacy-preserving model training.
• Utilize model averaging and aggregation strategies.
• Implement client-server architectures for federated learning systems.
• Design and build federated learning models for distributed data.
• Optimize federated learning for communication efficiency and scalability.
• Troubleshoot and address common challenges in federated learning.
• Implement privacy-preserving techniques in federated learning.
• Integrate federated learning with real-world applications and datasets.
• Understand how to handle data heterogeneity and non-IID data in federated learning.
• Explore advanced federated learning techniques (e.g., differential privacy, split learning).
• Apply real world use cases for federated learning in various domains.
• Leverage federated learning frameworks and tools for efficient development.

Duration

10 Days

Course content

Module 1: Introduction to Federated Learning

• Fundamentals of federated learning.
• Overview of secure aggregation, model averaging, and client-server architectures.
• Setting up a federated learning development environment.
• Introduction to federated learning frameworks and tools.
• Best practices for federated learning.

Module 2: Secure Aggregation Techniques

• Implementing secure aggregation techniques for privacy-preserving model training.
• Utilizing homomorphic encryption and secure multi-party computation.
• Designing and building secure aggregation protocols.
• Optimizing secure aggregation for communication efficiency.
• Best practices for secure aggregation.

Module 3: Model Averaging and Aggregation

• Implementing model averaging and aggregation strategies.
• Utilizing weighted averaging and federated averaging algorithms.
• Designing and building model aggregation pipelines.
• Optimizing aggregation for model convergence and accuracy.
• Best practices for model aggregation.

Module 4: Client-Server Architectures

• Implementing client-server architectures for federated learning systems.
• Utilizing distributed training frameworks (Flower, TensorFlow Federated).
• Designing and building scalable federated learning architectures.
• Optimizing client-server communication and coordination.
• Best practices for client-server architectures.

Module 5: Federated Learning Model Design

• Designing and building federated learning models for distributed data.
• Implementing model partitioning and distributed training strategies.
• Utilizing model adaptation techniques for heterogeneous data.
• Optimizing models for federated learning environments.
• Best practices for model design.

Module 6: Communication Efficiency and Scalability

• Optimizing federated learning for communication efficiency and scalability.
• Utilizing model compression and sparsification techniques.
• Implementing asynchronous and hierarchical federated learning.
• Designing scalable federated learning systems.
• Best practices for communication efficiency.

Module 7: Troubleshooting Federated Learning Challenges

• Debugging common challenges in federated learning.
• Analyzing model convergence and communication issues.
• Utilizing troubleshooting techniques for problem resolution.
• Resolving common federated learning challenges.
• Best practices for troubleshooting.

Module 8: Privacy-Preserving Techniques

• Implementing privacy-preserving techniques in federated learning.
• Utilizing differential privacy and local differential privacy.
• Designing and building privacy-preserving federated learning systems.
• Optimizing privacy-preserving mechanisms for accuracy.
• Best practices for privacy.

Module 9: Integration with Real-World Applications

• Integrating federated learning with real-world applications and datasets.
• Utilizing APIs and data connectors.
• Implementing federated learning in healthcare and finance.
• Optimizing integration for specific application domains.
• Best practices for integration.

Module 10: Handling Data Heterogeneity

• Understanding how to handle data heterogeneity and non-IID data in federated learning.
• Utilizing data sampling and weighting techniques.
• Designing and building robust federated learning algorithms.
• Optimizing models for non-IID data distributions.
• Best practices for data heterogeneity.

Module 11: Advanced Federated Learning Techniques

• Exploring advanced federated learning techniques (differential privacy, split learning).
• Utilizing differential privacy for rigorous privacy guarantees.
• Implementing split learning for collaborative model training.
• Designing and building advanced federated learning systems.
• Optimizing advanced techniques for specific applications.
• Best practices for advanced techniques.

Module 12: Real-World Use Cases

• Implementing federated learning for mobile device applications.
• Utilizing federated learning for healthcare data analysis.
• Implementing federated learning for financial risk modeling.
• Utilizing federated learning for IoT data processing.
• Best practices for real-world applications.

Module 13: Federated Learning Frameworks and Tools Implementation

• Utilizing TensorFlow Federated and Flower for federated learning.
• Implementing federated learning algorithms with frameworks.
• Designing and building federated learning pipelines.
• Optimizing tool usage for efficient development.
• Best practices for framework implementation.

Module 14: Model Evaluation and Monitoring

• Implementing model evaluation and monitoring for federated learning.
• Utilizing metrics for model accuracy and privacy preservation.
• Designing and building monitoring dashboards.
• Optimizing monitoring for real-time insights.
• Best practices for monitoring.

Module 15: Future Trends in Federated Learning

• Emerging trends in federated learning.
• Utilizing AI for automated federated learning.
• Implementing federated learning in edge computing environments.
• Best practices for future applications.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.