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
- Contrast between traditional AI training methodologies and federated learning.
- Core principles and key advantages of the federated approach.
- Practical applications of federated learning in Edge AI contexts.
Architecture and Workflow of Federated Learning
- Analysis of client-server and peer-to-peer federated learning architectures.
- Strategies for data partitioning and decentralized model training.
- Examination of communication protocols and model aggregation techniques.
Implementing Federated Learning with TensorFlow Federated
- Configuration of TensorFlow Federated for distributed AI workloads.
- Construction of federated models using Python.
- Simulation of federated learning processes on edge hardware.
Federated Learning via PyTorch and OpenFL
- Overview of OpenFL capabilities for federated scenarios.
- Development of federated models based on PyTorch.
- Customization of federated aggregation algorithms.
Performance Optimization for Edge AI
- Leveraging hardware acceleration to boost federated learning performance.
- Strategies to minimize communication overhead and reduce latency.
- Adaptive learning techniques suited for resource-limited devices.
Data Privacy and Security in Federated Learning
- Application of privacy-preserving methods, including Secure Aggregation, Differential Privacy, and Homomorphic Encryption.
- Mitigation of data leakage vulnerabilities in federated AI models.
- Adherence to regulatory standards and ethical guidelines.
Deployment of Federated Learning Systems
- Setup of federated learning on physical edge devices.
- Continuous monitoring and updating of federated models.
- Scaling federated learning deployments for enterprise-level requirements.
Future Trends and Case Studies
- Emerging research directions in federated learning and Edge AI.
- Analysis of real-world implementations in healthcare, finance, and IoT.
- Roadmap for advancing federated learning solutions.
Summary and Next Steps
Requirements
- Profound grasp of machine learning and deep learning fundamentals.
- Proficiency in Python programming and AI frameworks such as PyTorch, TensorFlow, or comparable tools.
- Foundational knowledge of distributed computing and network architectures.
- Familiarity with data privacy and security principles in the context of AI.
Target Audience
- AI researchers
- Data scientists
- Security specialists
Testimonials (1)
That we can cover advance topic and work with real-life example
