Federated Understanding
A new computational paradigm. Not centralized analysis, which moves all data. Not federated learning, which moves model parameters. Federated understanding moves question-shaped understanding fragments—and nothing else.
Three Paradigms, Orders of Magnitude Apart
Network traffic under federated understanding is O(I(D; A_Q)), compared to O(H(D)) for federated learning and O(|D|) for centralization. Since I(D; A_Q) ≪ H(D) ≪ |D| for surgical questions, the reduction is orders of magnitude.
Network Transfer Comparison
Scaling With Source Count
Why This Matters
Structural Privacy
Irrelevant data is never processed—not merely protected with noise. This is stronger than differential privacy: there is no privacy-utility trade-off because irrelevant information never enters the computation.
No Integration Step
All modalities map to S-space through observe bridges. Integration is composition—a built-in categorical operation. There is no separate ETL pipeline, no schema matching, no data harmonization.
No Stale Data
Representations are generated on demand from the current question against the current data. There is no cached representation that can become outdated.
Reproducibility by Construction
The trajectory T* encodes complete methodological provenance. Reproducing the analysis requires only the protocol specification and access to the data sources.