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.
Duration 21 hours
Course Outline
Introduction to Federated Learning
- Comparison of conventional AI training methodologies with federated learning frameworks
- Fundamental tenets and strategic advantages of federated learning
- Application scenarios for federated learning in Edge AI contexts
Federated Learning Architecture and Workflow
- Analysis of client-server and peer-to-peer federated learning structures
- Data partitioning mechanisms and decentralized model training processes
- Communication protocols and model aggregation strategies
Implementing Federated Learning with TensorFlow Federated
- Configuration of TensorFlow Federated for distributed AI training environments
- Development of federated learning models using Python
- Simulation of federated learning operations on edge devices
Federated Learning with PyTorch and OpenFL
- Overview of the OpenFL framework for federated learning implementations
- Execution of PyTorch-based federated models
- Customization of federated aggregation techniques
Optimizing Performance for Edge AI
- Hardware acceleration strategies for federated learning workloads
- Reduction of communication overhead and network latency
- Adaptive learning strategies for resource-constrained devices
Data Privacy and Security in Federated Learning
- Privacy-preserving mechanisms (Secure Aggregation, Differential Privacy, Homomorphic Encryption)
- Mitigation of data leakage risks in federated AI models
- Regulatory compliance standards and ethical governance considerations
Deploying Federated Learning Systems
- Deployment of federated learning on physical edge hardware
- Monitoring and iterative updating of federated models
- Scaling federated learning deployments for enterprise-scale operations
Future Trends and Case Studies
- Emerging research directions in federated learning and Edge AI
- Empirical case studies in healthcare, finance, and the Internet of Things
- Strategic next steps for advancing federated learning solutions
Summary and Recommended Actions
Requirements
- Profound understanding of machine learning and deep learning methodologies
- Demonstrated experience with Python programming and AI frameworks (such as PyTorch, TensorFlow, or equivalent)
- Foundational knowledge of distributed computing and network architecture
- Familiarity with data privacy and security principles within artificial intelligence systems
Target Audience
- AI researchers
- Data scientists
- Security specialists
Testimonials (1)
That we can cover advance topic and work with real-life example
