Introduction to Federated Learning Training Course

Introduction to Federated Learning

• Overview of Federated Learning concepts for government applications
• Comparison of decentralized model training with traditional centralized approaches
• Benefits of Federated Learning in enhancing privacy and data security for government operations

Basic Federated Learning Algorithms

• Introduction to Federated Averaging for government use cases
• Steps for implementing a simple Federated Learning model for government agencies
• Comparison of Federated Learning with traditional machine learning methods in the public sector

Data Privacy and Security in Federated Learning

• Understanding data privacy concerns in artificial intelligence for government applications
• Techniques for enhancing privacy in Federated Learning for government datasets
• Secure aggregation and data encryption methods tailored for government use

Practical Implementation of Federated Learning

• Setting up a Federated Learning environment for government agencies
• Building and training a Federated Learning model for government projects
• Deploying Federated Learning in real-world scenarios within the public sector

Challenges and Limitations of Federated Learning

• Addressing non-IID data challenges in Federated Learning for government datasets
• Managing communication and synchronization issues in government networks
• Scaling Federated Learning solutions for large government systems

Case Studies and Future Trends

• Case studies of successful Federated Learning implementations in the public sector
• Exploring the future of Federated Learning for government applications
• Emerging trends in privacy-preserving AI for government use

Summary and Next Steps