What you’ll learn in this course series
Join Federated Learning! In this two-part course series, you will use Flower, a popular open source framework, to build a federated learning system, and learn about federated fine-tuning of LLMs with private data in part two.
Federated learning allows models to be trained across multiple devices or organizations without sharing data, improving privacy and security. Federated learning also has many practical uses, such as training next-word prediction models on mobile keyboards without transmitting sensitive keystrokes onto a central server.
First, you’ll learn about the federated training process, how to tune and customize it, how to increase data privacy, and how to manage bandwidth usage in federated learning.
Then, you’ll learn to apply federated learning to LLMs. You’ll explore challenges like data memorization and the computational resources required by LLMs, and explore techniques for efficiency and privacy enhancement, such as Parameter-Efficient Fine-Tuning (PEFT) and Differential Privacy (DP).
This two-part course series is self-contained. If you already know what federated learning is, you can start directly with part two of the course.
New Course! Enroll in Federated Learning