The surge in data-intensive machine learning (ML) applications necessitates effective incentives for data owners (DOs) to contribute data and train ML models collaboratively. The decision to participate in collaboration depends on the balance between utility gains and privacy loss. This project focuses on federated learning (FL), where DOs participate in collaborative learning without sharing raw […]
Read More