Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Federated Learning
- Comparison of traditional AI training methods with federated learning
- Core principles and benefits of federated learning
- Applications of federated learning in Edge AI for government
Federated Learning Architecture and Workflow
- Exploring client-server and peer-to-peer models in federated learning
- Strategies for data partitioning and decentralized model training
- Communication protocols and aggregation techniques
Implementing Federated Learning with TensorFlow Federated
- Configuring TensorFlow Federated for distributed AI training in government environments
- Constructing federated learning models using Python
- Simulating federated learning on edge devices
Federated Learning with PyTorch and OpenFL
- Overview of OpenFL for federated learning in government applications
- Developing PyTorch-based federated models
- Customizing federated aggregation methods
Optimizing Performance for Edge AI
- Enhancing hardware acceleration for federated learning
- Minimizing communication overhead and latency
- Implementing adaptive learning strategies for resource-constrained devices
Data Privacy and Security in Federated Learning
- Privacy-preserving techniques such as Secure Aggregation, Differential Privacy, and Homomorphic Encryption
- Addressing data leakage risks in federated AI models
- Ensuring regulatory compliance and ethical considerations for government
Deploying Federated Learning Systems
- Establishing federated learning on real edge devices for government use
- Monitoring and updating federated models in a secure manner
- Scaling federated learning deployments in enterprise environments for government
Future Trends and Case Studies
- Current research trends in federated learning and Edge AI for government applications
- Real-world case studies in healthcare, finance, and IoT for government sectors
- Next steps for advancing federated learning solutions for government
Summary and Next Steps
Requirements
- A strong understanding of machine learning and deep learning concepts for government applications.
- Experience with Python programming and AI frameworks such as PyTorch, TensorFlow, or similar tools.
- Basic knowledge of distributed computing and networking principles.
- Familiarity with data privacy and security concepts in the context of AI for government use.
Audience
- AI researchers
- Data scientists
- Security specialists
21 Hours
