Credits: 3
Principles and applications of federated learning. Federated optimization, statistical and system homogeneity models, variations of federated aggregation, security and privacy considerations, foundation models.
Description
Prerequisite: ENEE436 or CMSC422; or students who have taken courses with comparable content may contact the Department. Cross-listed with CMSC848Z. Credit only granted for ENEE759Z or CMSC848Z.Principles and applications of federated learning. Federated optimization, statistical and system homogeneity models, variations of federated aggregation, security and privacy considerations, foundation models.
Semesters Offered
Fall 2024
Testudo