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Course Outline
Introduction to Federated Learning
- Comparison of conventional AI training methods with federated learning approaches
- Core principles and operational benefits of federated learning
- Application scenarios for federated learning within Edge AI contexts, particularly for government systems
Federated Learning Architecture and Workflow
- Analysis of client-server and peer-to-peer federated learning frameworks
- Data partitioning methods and decentralized model training procedures
- Communication protocols and model aggregation strategies
Implementing Federated Learning with TensorFlow Federated
- Configuration of TensorFlow Federated for distributed AI training workflows
- Development of federated learning models using Python programming
- Simulation of federated learning processes on edge device infrastructure
Federated Learning with PyTorch and OpenFL
- Overview of OpenFL capabilities for federated learning initiatives
- Implementation of PyTorch-based federated models for public sector use cases
- Customization of federated aggregation techniques to meet specific requirements
Optimizing Performance for Edge AI
- Utilization of hardware acceleration for efficient federated learning operations
- Strategies to minimize communication overhead and reduce latency
- Adaptive learning protocols designed for resource-constrained devices commonly used in field operations
Data Privacy and Security in Federated Learning
- Implementation of privacy-preserving technologies, including Secure Aggregation, Differential Privacy, and Homomorphic Encryption, for government data protection
- Mitigation of data leakage risks within federated AI models to ensure information integrity
- Adherence to regulatory compliance standards and ethical guidelines for data handling
Deploying Federated Learning Systems
- Installation and configuration of federated learning infrastructure on operational edge devices
- Procedures for monitoring system performance and updating federated models
- Scaling federated learning deployments across enterprise and government environments
Future Trends and Case Studies
- Emerging research directions in federated learning and Edge AI technologies
- Examination of real-world case studies within healthcare, financial services, and Internet of Things (IoT) sectors
- Strategic recommendations for advancing federated learning solutions for government applications
Summary and Next Steps
Requirements
- Demonstrated proficiency in machine learning and deep learning methodologies
- Practical experience utilizing Python programming and artificial intelligence frameworks, such as PyTorch or TensorFlow
- Foundational knowledge of distributed computing architectures and network protocols
- Understanding of data privacy regulations and security principles applicable to AI systems, particularly for government applications
Audience
- Artificial intelligence researchers
- Data scientists
- Security specialists
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
