May 12, 2023, 5:25 p.m. | Ken Liu

Machine Learning Blog | ML@CMU | Carnegie Mellon University blog.ml.cmu.edu

TL;DR: We study the use of differential privacy in personalized, cross-silo federated learning (NeurIPS’22), explain how these insights led us to develop a 1st place solution in the US/UK Privacy-Enhancing Technologies (PETs) Prize Challenge, and share challenges and lessons learned along the way. If you are feeling adventurous, checkout the extended version of this post with more technical details! How can we be better prepared for the next pandemic? Patient data collected by groups such as hospitals and health agencies …

challenge challenges differential privacy federated learning insights lessons learned machine learning neurips personalization personalized pets privacy prize research retrospective solution study technologies

Founding AI Engineer, Agents

@ Occam AI | New York

AI Engineer Intern, Agents

@ Occam AI | US

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Sr. BI Analyst

@ AkzoNobel | Pune, IN