Introduction

Federated learning is an emerging machine learning (ML) technique that allows models to be trained across multiple decentralized devices or servers while keeping data localized. This innovative approach addresses privacy concerns by ensuring that sensitive data never leaves the device or server. As data privacy regulations become more stringent and the need for collaborative models grows, federated learning is gaining traction in various industries, including healthcare, finance, and mobile applications.

This course provides an in-depth understanding of federated learning concepts, how it works, and practical applications across different sectors. Participants will learn about the architecture, algorithms, and tools used for federated learning, and gain hands-on experience in building and deploying federated models.

Objectives

By the end of this course, participants will:

• Understand the fundamental concepts and principles behind federated learning.
• Learn how federated learning differs from traditional centralized machine learning models.
• Gain experience with federated learning frameworks such as TensorFlow Federated and PySyft.
• Understand the challenges related to data privacy, communication efficiency, and model convergence in federated learning.
• Explore real-world applications of federated learning in fields such as healthcare, finance, and IoT.
• Learn how to implement federated learning in practice through hands-on sessions.

Who Should Attend?

This course is designed for:

• Data scientists, machine learning engineers, and AI practitioners interested in exploring decentralized learning techniques.
• Researchers and developers in fields requiring privacy-sensitive data handling, such as healthcare, finance, and mobile computing.
• Professionals looking to expand their knowledge on advanced machine learning concepts and privacy-preserving AI methods.
• Anyone looking to stay ahead in the rapidly evolving field of federated learning and its applications.

Day 1: Introduction to Federated Learning

Morning Session: Understanding Federated Learning

• Overview of federated learning: The need for decentralized learning.
• Centralized vs. decentralized machine learning models.
• Key concepts in federated learning: Local model training, aggregation, and privacy.
• Benefits and challenges of federated learning in comparison to traditional methods.
• Real-world use cases of federated learning (e.g., mobile applications, healthcare, IoT).
• Privacy and security considerations: Differential privacy, secure aggregation, and homomorphic encryption.

Afternoon Session: Federated Learning Architecture

• Components of federated learning architecture: Clients, servers, and aggregators.
• How data stays localized on clients: Data partitioning and federated averaging.
• Federated learning vs. other privacy-preserving techniques: Differential privacy, multi-party computation, etc.
• Hands-on: Exploring a simple federated learning framework and its components (e.g., TensorFlow Federated, PySyft).

Day 2: Federated Learning Algorithms and Approaches

Morning Session: Federated Learning Algorithms

• The federated learning workflow: Local training, aggregation, and model updates.
• Key algorithms in federated learning: Federated averaging (FedAvg), Federated SGD, and others.
• Techniques for improving convergence and communication efficiency.
• Addressing challenges like straggler problems, non-IID data, and model heterogeneity.
• Hands-on: Implementing a simple federated learning algorithm in TensorFlow Federated.

Afternoon Session: Communication Efficiency and Privacy

• Challenges in federated learning: Communication costs and privacy trade-offs.
• Techniques for reducing communication costs: Compression, quantization, and model pruning.
• Privacy-preserving federated learning: Differential privacy, secure aggregation, and federated learning with encryption.
• Hands-on: Experimenting with federated learning model optimization for communication efficiency.

Day 3: Implementing Federated Learning Frameworks

Morning Session: TensorFlow Federated (TFF)

• Introduction to TensorFlow Federated: Setting up the environment and creating federated models.
• Federated learning with TFF: Designing federated computations and aggregating models.
• Hands-on: Implementing a federated learning model using TensorFlow Federated and evaluating performance.

Afternoon Session: PySyft and Federated Learning

• Introduction to PySyft: A library for privacy-preserving machine learning and federated learning.
• Federated learning with PySyft: How to implement federated learning in PyTorch and other frameworks.
• Hands-on: Creating a federated learning model using PySyft and comparing results with TensorFlow Federated.

Day 4: Real-World Applications and Challenges of Federated Learning

Morning Session: Federated Learning in Healthcare

• Federated learning in healthcare: Enabling collaborative machine learning with sensitive medical data.
• Real-world case studies: Federated learning in electronic health records (EHR) and medical imaging.
• Overcoming challenges in healthcare: Ensuring data privacy, data quality, and regulatory compliance (e.g., HIPAA).
• Hands-on: Simulating federated learning with healthcare data (e.g., medical records, diagnostics).

Afternoon Session: Federated Learning in Mobile and IoT Applications

• Federated learning for mobile apps: Enhancing user experience while preserving privacy.
• IoT and edge computing: How federated learning is applied to smart devices and connected systems.
• Real-world use cases: Google Gboard, Apple’s Siri, and autonomous vehicles.
• Hands-on: Building a federated learning model for an IoT or mobile application.

Day 5: Future Trends, Challenges, and Best Practices

Morning Session: Advanced Topics in Federated Learning

• Federated learning with non-IID (non-independent and identically distributed) data.
• Techniques for federated learning with unstructured data (images, audio, etc.).
• Federated learning with heterogeneous models and devices: Client and model diversity.
• Future of federated learning: Decentralized AI, blockchain integration, and multi-party computation.

Afternoon Session: Ethical Considerations and Implementation Best Practices

• Ethical concerns in federated learning: Transparency, fairness, and accountability.
• Best practices for implementing federated learning in real-world systems.
• Overcoming challenges: Managing decentralized systems, privacy breaches, and data silos.
• Hands-on: Final project: Participants will apply federated learning to a business problem or industry-specific challenge.
• Course wrap-up, Q&A, and certification.

Materials and Tools:

• Software and Tools: TensorFlow Federated, PySyft, PyTorch, Python, Jupyter Notebooks.
• Resources: Course slides, hands-on exercises, code examples, datasets for federated learning simulations.
• Case Studies: Real-world examples of federated learning in healthcare, mobile, and IoT applications.
• Community Support: Access to a course forum for collaboration, discussions, and sharing knowledge.

Post-Course Support:

• Access to recorded sessions and course materials.
• Follow-up webinars to explore advanced federated learning topics.
• Ongoing community support and collaboration via discussion forums.
• Mentoring sessions for help with applying federated learning concepts in specific projects or organizations.