Federated Skill Training
by Nick Clark | Published April 25, 2026
Skill training operates federated across mesh participants. Each participant contributes training observations under credentialed identity; the federated training produces adaptations admitted by all contributing participants.
What It Specifies
Federated training carries: participating participants, contributed observations, training authority, resulting adaptations, and signatures from all contributing participants. The resulting adaptations admit across all participants.
Federation is governance-credentialed. The federation authority, the participant contributions, and the resulting adaptations all enter lineage; downstream operations admit against the federated chain.
Why It Matters Structurally
Centralized training produces structural problems: data centralization (privacy, regulatory burden), single training authority (capture risk), data-locality constraints (cross-jurisdiction friction).
Federated training produces structural decomposition. Each participant retains its observations; federation produces shared adaptations; the resulting operation respects data locality.
How It Composes With Mesh Operation
The architecture defines the federation protocol, the contribution-credentialing format, and the resulting-adaptation distribution. Implementations apply the architecture; federated training operates within the framework.
Federation composes with other features. Cross-jurisdictional federation, byzantine-robust federated training under disputed contributions, and dispute mechanism for federation disputes all build on the federation primitive.
What This Enables
Cross-organization adaptation training, cross-jurisdiction adaptation training, and coalition adaptation training all gain structurally-supported federation.
The architecture also supports federation evolution. As federated-learning techniques mature, federation protocols update through governance procedures.
