Convex Loss

Convex loss functions are central to many machine learning algorithms, aiming to simplify optimization by ensuring a single global minimum. Current research focuses on extending the applicability of convex loss methods to non-convex scenarios, particularly within federated learning and differentially private settings, often employing techniques like gradient coding, variance reduction, and robust loss functions. This work is significant because it addresses challenges in privacy, robustness, and efficiency, impacting the development of more reliable and scalable machine learning models for diverse applications. The development of efficient algorithms for handling non-convex losses and heterogeneous data is a key area of ongoing investigation.