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Course Outline
Introduction to Federated Learning
- Comparison of traditional AI training methods against federated learning approaches.
- Core principles and key advantages of federated learning.
- Practical use cases for federated learning in Edge AI applications.
Federated Learning Architecture and Workflow
- Examination of client-server and peer-to-peer federated learning models.
- Data partitioning strategies and decentralized model training mechanisms.
- Communication protocols and model aggregation strategies.
Implementing Federated Learning with TensorFlow Federated
- Configuring TensorFlow Federated for distributed AI training environments.
- Developing federated learning models with Python.
- Simulating federated learning processes on edge devices.
Federated Learning with PyTorch and OpenFL
- Overview of OpenFL for federated learning implementations.
- Building federated models based on PyTorch.
- Tailoring federated aggregation techniques to specific requirements.
Optimizing Performance for Edge AI
- Leveraging hardware acceleration for federated learning tasks.
- Strategies to minimize communication overhead and latency.
- Adaptive learning strategies for devices with limited resources.
Data Privacy and Security in Federated Learning
- Exploration of privacy-preserving techniques, including Secure Aggregation, Differential Privacy, and Homomorphic Encryption.
- Mitigating risks associated with data leakage in federated AI models.
- Considerations regarding regulatory compliance and ethical standards.
Deploying Federated Learning Systems
- Establishing federated learning on actual edge hardware.
- Continuous monitoring and updating of federated models.
- Scaling federated learning deployments for enterprise-level environments.
Future Trends and Case Studies
- Insights into emerging research in federated learning and Edge AI.
- Analysis of real-world case studies in sectors such as healthcare, finance, and IoT.
- Guidance on next steps for advancing federated learning solutions.
Summary and Next Steps
Requirements
- A robust command of machine learning and deep learning concepts.
- Practical experience with Python programming and AI frameworks such as PyTorch, TensorFlow, or comparable tools.
- Foundational knowledge of distributed computing and networking principles.
- Familiarity with data privacy and security standards within the AI domain.
Target Audience
- AI researchers.
- Data scientists.
- Security specialists.
21 Hours
Testimonials (1)
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