Federated Learning Basics for Distributed Large Language Models

Training large language models often requires vast amounts of data, but moving sensitive information to a central server poses massive privacy and compliance risks. Federated learning solves this by bringing the model to the data, allowing collaborative training without exposing private data. This text-based course guides you through the core concepts and practical workflows needed to implement distributed training safely. You will start by understanding foundational privacy concepts, local updates, and secure aggregation before moving on to modern fine-tuning techniques. What you'll learn: Understand the core architecture of federated learning and distributed data systems; Apply federated fine-tuning techniques to large language models; Configure secure aggregation algorithms to safely combine model updates; Implement privacy-preserving techniques like differential privacy and secure multi-party computation; Troubleshoot communication bottlenecks and optimize network efficiency during training rounds. This course begins with essential terminology and the mathematical foundations of decentralized training, then guides you through step-by-step written exercises to build and configure your own federated learning workflows. This course is designed for software developers, data scientists, and machine learning beginners who want to build privacy-first AI applications without needing complex prerequisites. Start reading today to master the future of private, distributed machine learning.