Federated learning

This wiki entry defines a term used across VSAVM and explains why it matters in the architecture.

The diagram has a transparent background and highlights the operational meaning of the term inside VSAVM.

Related wiki pages: VM, event stream, VSA, bounded closure, consistency contract.

Definition

Federated learning trains across clients without centralizing raw data, using aggregated updates or artifacts.

Role in VSAVM

VSAVM can federate discrete statistics, VSA prototypes, and executable modules such as schemas and macro programs. This aligns naturally with modular learning and auditability.

Mechanics and implications

The main risk is rule pollution. VSAVM mitigates this by requiring closure-based health checks before promoting new rules into a shared library. Modules can be versioned and rolled back more transparently than dense parameter deltas.

Further reading

Federated learning is often paired with privacy techniques such as differential privacy. VSAVM’s approach emphasizes federating explicit artifacts with governance via consistency checks.

References

Federated learning (Wikipedia) Differential privacy (Wikipedia) Privacy (Wikipedia)