Introduction:

In the age of data-driven decision-making, the need to protect privacy while harnessing the power of AI has become critical. Federated Learning (FL) is an innovative technique that enables machine learning models to be trained collaboratively across decentralized devices or servers, while keeping data localized and private. This approach addresses privacy concerns by ensuring that sensitive data never leaves its source. This course delves into Federated Learning and explores various privacy-preserving AI techniques such as differential privacy, homomorphic encryption, and secure multi-party computation (SMPC), providing participants with the tools to build AI systems that prioritize data security and privacy.

Course Objectives:

• Understand the fundamentals of Federated Learning (FL) and its application in privacy-preserving machine learning.
• Explore the key concepts of privacy-preserving AI, including differential privacy, homomorphic encryption, and secure computation.
• Learn how to implement federated learning systems to train models on decentralized data without compromising privacy.
• Gain hands-on experience with privacy-preserving algorithms and Federated Learning frameworks.
• Address the challenges and ethical considerations of using federated learning and privacy-preserving techniques in real-world applications.
• Explore the latest research and future trends in privacy-preserving AI and its impact on industries like healthcare, finance, and IoT.

Who Should Attend?

This course is suitable for:

• AI/ML Engineers and Data Scientists interested in privacy-preserving techniques and Federated Learning.
• Security and Privacy Professionals seeking to understand how to protect sensitive data in machine learning models.
• Software Developers and System Architects working on distributed systems or collaborative AI applications.
• Business Leaders and Regulatory Experts aiming to stay compliant with privacy regulations like GDPR and HIPAA.
• Researchers focused on the cutting-edge applications of privacy-preserving AI.

Course Outline:

Day 1: Introduction to Federated Learning and Privacy-Preserving AI

• Session 1: What is Federated Learning?

  • Overview of Federated Learning and its role in decentralized machine learning.
  • Traditional centralized learning vs. Federated Learning: The challenge of data silos and privacy.
  • Key components of FL: Local model training, aggregation, and communication between devices.
  • Use cases for Federated Learning in healthcare, finance, and mobile applications.

• Session 2: Privacy-Preserving AI Concepts

  • The need for privacy in machine learning: Risks of data exposure and breaches.
  • Introduction to privacy-preserving AI techniques: Differential privacy, homomorphic encryption, and secure multi-party computation.
  • Ethical considerations and the importance of data protection in AI development.

• Session 3: Federated Learning Frameworks and Tools

  • Introduction to FL frameworks: Google’s TensorFlow Federated (TFF), PySyft, and Federated AI Technology Enabler (FATE).
  • Setting up a basic Federated Learning system: Components, communication protocols, and model aggregation.
  • Hands-on introduction to TensorFlow Federated and PySyft for building simple FL models.

Day 2: Federated Learning Architecture and Implementation

• Session 1: Understanding Federated Learning Architecture

  • Decentralized and server-client architectures in FL: Data aggregation, client-side training, and server-side model updates.
  • Federated Learning lifecycle: Model initialization, training, aggregation, and synchronization.
  • Challenges in Federated Learning: Communication costs, data heterogeneity, and device failures.

• Session 2: Training Federated Models

  • Data preparation for Federated Learning: Preprocessing data on local devices while ensuring privacy.
  • Federated Learning algorithms: Federated Averaging (FedAvg), model personalization, and client selection.
  • Techniques to improve convergence and scalability in federated systems.

• Session 3: Hands-on Workshop: Implementing Federated Learning

  • Implementing a basic Federated Learning model using TensorFlow Federated or PySyft.
  • Setting up local training on client devices and model aggregation on a central server.
  • Evaluating and improving model performance in a federated environment.

Day 3: Privacy-Preserving Techniques in AI

• Session 1: Differential Privacy in Machine Learning

  • What is differential privacy and how it works: The concept of noise addition and privacy guarantees.
  • Applications of differential privacy in data aggregation and model training.
  • Trade-offs between privacy and accuracy: Balancing utility and protection.

• Session 2: Homomorphic Encryption for Privacy-Preserving AI

  • Introduction to homomorphic encryption: Encrypting data while performing computations on it.
  • Use cases of homomorphic encryption in secure data sharing and computations.
  • Practical examples of implementing homomorphic encryption in machine learning models.

• Session 3: Secure Multi-Party Computation (SMPC)

  • What is SMPC and how it enables secure collaboration without revealing sensitive data?
  • Use cases: Collaborative data analysis, federated learning, and encrypted data processing.
  • Hands-on examples of implementing SMPC for secure collaborative AI model training.

Day 4: Advanced Topics in Federated Learning and Privacy-Preserving AI

• Session 1: Federated Learning with Privacy-Preserving Techniques

  • Integrating differential privacy and homomorphic encryption with Federated Learning.
  • Privacy-enhancing Federated Learning frameworks: Secure Aggregation and Federated Learning with Differential Privacy (FL-DP).
  • Practical considerations for implementing privacy-preserving Federated Learning in real-world environments.

• Session 2: Federated Learning for IoT and Edge Computing

  • How Federated Learning is applied to IoT networks and edge devices: Training AI models without transmitting sensitive data to centralized servers.
  • Edge-based model training and aggregation: Dealing with constrained devices and network latency.
  • Case study: Federated Learning in smart homes, autonomous vehicles, and healthcare devices.

• Session 3: Addressing the Challenges of Federated Learning

  • Data heterogeneity in Federated Learning: Dealing with non-i.i.d. (Independent and Identically Distributed) data.
  • Security risks and adversarial attacks in Federated Learning.
  • Techniques to mitigate challenges: Secure Aggregation, Byzantine Fault Tolerance, and model robustness.

Day 5: Real-World Applications and Future Directions

• Session 1: Federated Learning in Healthcare and Finance

  • Privacy-preserving Federated Learning for medical data: Training models on sensitive health data while maintaining patient privacy.
  • Federated Learning in financial institutions: Building credit risk models, fraud detection systems, and personalized financial recommendations.
  • Case studies on how Federated Learning is applied in healthcare, finance, and beyond.

• Session 2: Scalability, Governance, and Compliance in Federated Learning

  • Scaling Federated Learning systems: Handling large numbers of clients and data sources.
  • Governance frameworks and the importance of ensuring compliance with privacy laws (GDPR, HIPAA).
  • Building transparent and accountable Federated Learning systems that meet regulatory standards.

• Session 3: Future Trends in Privacy-Preserving AI

  • The future of Federated Learning and privacy-preserving AI: Integrating with blockchain, AI ethics, and decentralized AI.
  • Emerging technologies in privacy-preserving machine learning: Federated Learning with Zero-Knowledge Proofs and Quantum-safe privacy techniques.
  • Industry trends and research directions in Federated Learning and privacy-preserving AI.

• Session 4: Final Project and Wrap-Up

  • Final group project: Design and implement a Federated Learning system with integrated privacy-preserving techniques for a real-world application.
  • Presentations of final projects and feedback from instructors.
  • Course wrap-up and future learning resources.