AAAI 460 - AICE: Federated Learning

When Offered: F, S, SS

3 Credit(s)
3 Lecture Hour(s)
0 Lab Hour(s)

This course provides an introduction to Federated Learning (FL), exploring its evolution from conceptualization to its first application and subsequent commercial adoption. Students will gain a comprehensive understanding of the fundamental principles of FL, and how it differs from traditional centralized machine learning. The course also addresses the real-world challenges encountered by federated learning practitioners, such as the trade-off between privacy and accuracy, security concerns, personalization techniques, and the importance of communication efficiency. Additionally, the course explores the interdisciplinary nature of federated learning, with particular emphasis on its role in the development of large language models (LLMs) and applications in various domains such as healthcare, finance, and mobile edge computing.

Prerequisite(s): COMP 365
Corequisite(s): none