Federated Learning for Privacy-Preserving AI Virtual Internship
In this virtual internship, students will learn how to implement federated learning techniques to train machine learning models without centralizing sensitive user data. They will gain hands-on experience in building privacy-preserving AI systems, which is a critical skill in today's data-driven world. Upon completion, students will be equipped with the knowledge and practical skills to develop secure and ethical AI applications.
Track Overview
Tasks & Milestones
Explore Federated Learning Concepts
IntermediateIn this task, students will learn about the core concepts of federated learning and its advantages over traditional centralized machine learning.
Implement Federated Averaging Algorithm
IntermediateIn this task, students will implement the Federated Averaging algorithm using TensorFlow and evaluate its performance on a benchmark dataset.
Implement Federated Distillation
IntermediateIn this task, students will implement the Federated Distillation algorithm using PyTorch and evaluate its performance on a benchmark dataset.
Implement Secure Aggregation Protocol
IntermediateIn this task, students will implement a secure aggregation protocol for federated learning using TensorFlow Privacy.
Implement Differential Privacy for Federated Learning
IntermediateIn this task, students will implement a differential privacy mechanism for federated learning using OpenMined.
Analyze Federated Learning Case Studies
IntermediateIn this task, students will analyze real-world case studies of federated learning and present their findings.
Develop a Federated Learning Deployment Plan
IntermediateIn this task, students will develop a deployment plan for a federated learning application.
Prerequisites
- • Proficiency in Python programming
- • Familiarity with machine learning concepts and algorithms
- • Basic understanding of data privacy and security
Certificate
Certificate of Completion
Earn a certificate upon successful completion