DISTRIBUTED MACHINE LEARNING

Catalog description

This course introduces students to the algorithms, systems, and privacy challenges of distributed and federated machine learning. Students will learn both the theoretical underpinnings of distributed optimization (SGD and its variants) and practical frameworks for building and deploying federated learning systems. A major focus will be hands-on labs and an individual final project using publicly available large-scale datasets. Students will implement and evaluate federated optimization methods such as FedSGD and FedAvg, explore communication-efficient algorithms, and fine-tune models in a distributed setting. PREREQ: COMPSCI 432 AND STAT 342