On privacy and personalization in federated learning: a retrospective on the US/UK PETs challenge

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! Patient data collected by groups such as hospitals and health agencies is a critical tool for monitoring and preventing the spread of disease. Unfortunately, while this data contains a wealth of useful information for disease forecasting, the data itself may be highly sensitive and stored in disparate locations (e.g., across multiple hospitals, health agencies, and districts). In this post we discuss our research on federated learning, which aims to tackle this challenge by performing decentralized learning across private data silos. We then explore an application of our research to the problem of privacy-preserving pandemic forecasting--a scenario where we recently won a 1st place, $100k prize in a competition hosted by the US & UK governments--and end by discussing several directions of future work based on our experiences.