Implementation Challenges in Privacy-Preserving Federated Learning

This post interviews researchers and practitioners about privacy-preserving federated learning (PPFL), highlighting difficulties in defining and comparing realistic threat models, the theory–reality gap in deploying PPFL systems, risks from bespoke or retrofitted system designs, and the need for privacy-by-design, robust threat modeling, and maturing open-source frameworks and collaborative efforts to make PPFL deployments more secure and practical.