Thinking about this some more, such federated training will require more careful differentiation between two training modes that have been traditionally clumped

Thinking about this some more, such federated training will require more careful differentiation between two training modes that have been traditionally clumped together: 1. Model development, improvements and tuning (choosing correct preprocessing, features, architecture, introspection, debugging…). Client-side unsuitable. 2. Training / updating of fixed, well-designed models on new "unseen-by-human-but-otherwise-well-behaved" data. Client-side suitable. So far, the former has always been the bigger challenge, hence my QA concerns. But perhaps once your problem is well-understood, and data known to be equally-distributed and well-behaved, the latter becomes increasingly useful. It's definitely an exciting new direction. The OP identifies personal / PII data as the flagship use-case, which sounds about right.