Privacy Attacks in Federated Learning

This post is part of a series on privacy-preserving federated learning. The series is a collaboration between NIST and the UK government’s Centre for Data Ethics and Innovation. Learn more and read all the posts published to date at NIST’s Privacy Engineering Collaboration Space or the CDEI blog . Our first post in the series introduced the concept of federated learning—an approach for training AI models on distributed data by sharing model updates instead of training data. At first glance, federated learning seems to be a perfect fit for privacy since it completely avoids sharing data