A. M.

Convex Optimization Methods for Privacy-Preserving Federated Learning

This work provides a unified approach for aiding the design of state-of-the-art privacy-preserving distributed learning systems that are also utility-optimal and is an important step towards using such approaches in high-stakes domains like healthcare or finance.