Federated Learning and Edge AI Training Course

Introduction to Federated Learning

• Comparison of traditional AI training methods with federated learning approaches
• Core principles and benefits of federated learning for government
• Applications of federated learning in Edge AI for public sector operations

Federated Learning Architecture and Workflow

• Examination of client-server and peer-to-peer models in federated learning
• Techniques for data partitioning and decentralized model training in government contexts
• Communication protocols and aggregation strategies for secure and efficient model updates

Implementing Federated Learning with TensorFlow Federated

• Configuration of TensorFlow Federated for distributed AI training in public sector projects
• Development of federated learning models using Python for government applications
• Simulation of federated learning on edge devices to enhance operational efficiency

Federated Learning with PyTorch and OpenFL

• Introduction to OpenFL for federated learning in government environments
• Implementation of PyTorch-based federated models for public sector use cases
• Customization of federated aggregation techniques to meet specific agency needs

Optimizing Performance for Edge AI

• Utilization of hardware acceleration for federated learning in government systems
• Strategies to reduce communication overhead and latency in edge deployments
• Development of adaptive learning strategies for resource-constrained devices in public sector settings

Data Privacy and Security in Federated Learning

• Implementation of privacy-preserving techniques such as Secure Aggregation, Differential Privacy, and Homomorphic Encryption for government data
• Measures to mitigate data leakage risks in federated AI models used by government agencies
• Compliance with regulatory requirements and ethical considerations in federated learning for public sector operations

Deploying Federated Learning Systems

• Setup of federated learning systems on real edge devices for government use
• Monitoring and updating federated models to ensure continuous improvement and security
• Scaling federated learning deployments in enterprise environments within government agencies

Future Trends and Case Studies

• Exploration of emerging research in federated learning and Edge AI for government applications
• Real-world case studies of federated learning in healthcare, finance, and IoT for public sector innovation
• Next steps for advancing federated learning solutions to support government missions

Summary and Next Steps