As machine learning models and the datasets used to train them continue to grow in size and complexity, relying on a single machine for computation becomes inefficient or even impossible. Training often requires distributing the workload across multiple processors or machines. This chapter focuses on the optimization algorithms and strategies designed specifically for these distributed environments.

You will learn about the motivations and challenges associated with distributed training. We will examine common architectural patterns, such as the parameter server model, used to coordinate computation among workers. A key focus will be understanding the differences and trade-offs between synchronous and asynchronous update schemes. We will also address the critical issue of communication overhead, discussing bottlenecks and techniques like gradient compression and efficient synchronization patterns like All-Reduce. Finally, we'll touch upon the unique optimization considerations within the framework of federated learning. The goal is to equip you with the knowledge to implement and analyze optimization methods suitable for large-scale, distributed machine learning tasks.