Federated Skill Training

1. Mechanism and Primitive Description

Federated skill training under the disclosed pattern produces shared adaptations — model parameters, policy updates, classifier weights, behavioral skills — without ever centralizing the underlying observations from which the contributions are derived. Each participating mesh trains locally on observations it holds under its own credentialing, produces a contribution (a gradient, a model delta, a distilled skill artifact) bounded by a declared differential-privacy-class admissibility, and submits the contribution to a federation aggregation governed by the chain.

There is no central training authority. The federation is constituted as a credentialing-authority graph in which participating meshes recognize one another under declared cross-recognition, and aggregation is performed by a federation operator whose authority to aggregate is itself credentialed and chain-recorded. Aggregation is not a privileged trust position; it is a governed actuation conditioned on composite admissibility of the contributions, and the resulting adaptation is admitted only when the chain's admissibility rules accept the aggregated artifact.

Each contribution carries authority-credentialed observation lineage: the contributing mesh's identity, the credentialing authority that admitted the contribution, the differential-privacy class under which the contribution was prepared (epsilon-delta budget, noise mechanism, sensitivity bound), and provenance edges referencing the credentialed-observation classes the local training drew on without exposing the observations themselves. Evidential weighting assigns each contribution an admissibility coefficient based on the contributor's standing, the privacy class it satisfies, the size and recency of its supporting evidence, and corroboration from independent contributors. Composite admissibility fuses contributions into the aggregated adaptation; governed actuation conditions adaptation publication and downstream skill use on that admissibility; lineage records every contribution, every aggregation event, and every resulting adaptation so that downstream operations using the skill can trace its provenance to credentialed origins.

Differential-privacy-class admissibility is treated as a structural admissibility category rather than as a contributor-side hygiene practice. The chain admits a contribution only when the contribution declares and demonstrably satisfies a privacy class that the federation's admissibility rule accepts; a contribution claiming a class it does not satisfy is structurally inadmissible, and a federation tightening its required class effectively excludes contributions that no longer qualify. This binds the privacy property to the chain rather than leaving it as an out-of-band guarantee.

2. Operating Parameters and Engineering Envelope

Differential-privacy classes are declared as a hierarchy with explicit (epsilon, delta) budgets, sensitivity bounds, and admissible noise mechanisms (Gaussian, Laplace, discrete Gaussian for integer-valued contributions, or composite mechanisms under advanced composition). Each class corresponds to a chain-admissibility rule specifying the minimum class a contribution must satisfy to be admitted into a given federation. High-sensitivity federations (medical, defense, regulated commerce) require strict classes with small epsilon and per-participant cumulative budgets tracked across federation rounds; lower-sensitivity federations may admit relaxed classes.

Aggregation parameters include round cadence (continuous, periodic, or event-triggered), participant-quorum thresholds (minimum number of credentialed contributors required for a round to produce an admissible adaptation), corroboration requirements (minimum independent contributors agreeing on the direction of the update within tolerance), and contribution-weighting schemes (equal weighting, evidence-weighted, authority-weighted). Aggregation mechanisms may include secure-aggregation protocols (cryptographic summation without exposing individual contributions), trusted-execution-environment aggregation, or chain-recorded clear aggregation depending on the federation's privacy posture.

The engineering envelope bounds round latency (training and aggregation cycle), contribution size (gradient compression and quantization), and federation breadth (number of meshes a single federation can scale to before admissibility evaluation becomes the bottleneck). Failure modes include byzantine contributions (a compromised mesh submitting adversarial updates), privacy-budget exhaustion (a participant exceeding cumulative epsilon and being structurally excluded until the budget is renewed), and credential-revocation cascades (contributions from a revoked authority losing admissibility, with downstream adaptations re-evaluated under reduced corroboration).

Adaptation distribution is itself a chain-recorded actuation. A federated adaptation is published with its full provenance, and a using mesh admits the adaptation only if the chain's admissibility rules accept the adaptation's lineage; a mesh may decline an adaptation whose contributing federation includes participants outside its declared cross-recognition.

3. Alternative Embodiments

Embodiments span model classes (neural-network parameter updates, decision-tree ensembles, reinforcement-learning policy updates, classical-ML coefficient updates, distilled skill artifacts for behavior cloning), federation topologies (peer-to-peer, hub-and-spoke with rotating hubs, hierarchical with regional aggregators feeding global), and privacy mechanisms (central differential privacy, local differential privacy, secure-aggregation augmented, trusted-execution-environment augmented).

Sector embodiments include cross-organization industrial-skill federation (operators sharing autonomous-vehicle behaviors without sharing operational footage), cross-jurisdiction healthcare-model federation (hospitals jointly training diagnostic models without sharing patient data), coalition defense-skill federation (allied operators training joint adaptations under coalition governance), and consumer-device federation (edge devices contributing under user-consent credentialing).

Embodiments may also vary in adaptation class. Continuous fine-tuning embodiments produce streaming adaptation updates; episodic-training embodiments produce versioned adaptations released on declared schedules; on-demand embodiments produce adaptations targeted to specific operational scenarios. The structural form — credentialed contributions, composite admissibility under differential-privacy class, chain-recorded aggregation, lineage-recorded distribution — is preserved across all of these.

Aggregator-rotation embodiments distribute the aggregation actuation across participants on a declared schedule, so that no single participant accumulates the operational role of aggregator over time; the rotation itself is chain-recorded and bounded under the federation's governance procedures. Shadow-federation embodiments allow a candidate participant to contribute for evaluation purposes without its contributions being admitted into the operational adaptation, providing a structural onboarding path that preserves admissibility properties.

4. Composition With Adjacent Primitives

Federated skill training composes with the broader spatial mesh's observation primitive by sourcing contributions from credentialed local-training operations whose observation lineage is admitted under the chain. It composes with the actuation primitive by treating adaptation use — a skill executed on a mesh — as a governed actuation conditioned on adaptation admissibility, so that a using mesh cannot deploy a skill whose provenance has been revoked.

Cross-jurisdictional federation composes with the chain-trust substrate by allowing federations whose participants span legal jurisdictions, with declared cross-recognition controlling which jurisdictions admit which contributions and with privacy classes calibrated to the strictest jurisdiction's requirements. Byzantine-robust federation composes corroboration thresholds with declared adversarial bounds: a federation tolerating a bounded fraction of compromised contributors falls back to higher-corroboration aggregation rather than admitting unverified updates.

Dispute-mechanism composition treats federation disputes — a participant claiming wrongful exclusion, an operator claiming an admitted adaptation contains adversarial influence, an authority claiming privacy-budget violation — as appellate evaluations against the chain-recorded provenance. Composition with the zero-trust device-management primitive ensures that contributions originate from currently-attesting devices, since a compromised device's contributions would lose admissibility automatically. Composition with marketplace primitives allows skill artifacts to be settled as commodity contributions under the governed-marketplace pattern, with provenance preserved across the marketplace boundary.

5. Prior-Art Distinctions

Conventional federated-learning systems (e.g., FedAvg-pattern schemes, cross-silo and cross-device federated learning) coordinate distributed training but typically rely on a central server or coordinator whose authority is platform-issued, not chain-credentialed, and whose admissibility logic is implementation-defined rather than structural. The disclosed pattern is distinct in that there is no privileged central authority; aggregation is itself a governed actuation under the chain, and admissibility derives from credentialed contributors and composite evaluation.

Differential-privacy frameworks for federated learning provide privacy guarantees on contributions but do not bind those guarantees to a chain-admissibility rule that conditions admission, distribution, and downstream use of the resulting adaptation. The disclosed pattern treats differential-privacy class as an admissibility property whose enforcement is structural rather than contractual.

Secure-aggregation and trusted-execution-environment federated-learning schemes address the confidentiality of individual contributions during aggregation but do not provide the broader credentialing, lineage, and composite admissibility framework. Decentralized-federated-learning proposals (peer-to-peer or blockchain-anchored) address coordinator-removal but typically substitute cryptographic finality for chain-credentialed admissibility. The disclosed pattern's distinction is the joint operation of the five chain properties applied to skill training, with differential-privacy class as a first-class admissibility category and with no central training authority required.

6. Disclosure Scope

The disclosure encompasses federated training of skills, models, policies, and adaptations across credentialed meshes under the five-property governance chain, with no central training authority required and with differential-privacy-class admissibility constraining contributions. The scope reaches embodiments across model classes, federation topologies, privacy mechanisms, and sector deployments, provided the joint chain operation and the differential-privacy-class admissibility are preserved.

The scope reaches embodiments in which round cadence, quorum thresholds, corroboration requirements, weighting schemes, and aggregation mechanisms vary by federation, provided contributions are credentialed, aggregation is governed, and adaptations are lineage-distributed. It reaches embodiments coupling federated training to adjacent primitives — observation, device admissibility, marketplace settlement of skill artifacts — through shared chain provenance.

The scope does not reach federated-learning systems in which a central coordinator's authority is platform-issued rather than chain-credentialed, systems in which contributions enter aggregation without differential-privacy-class admissibility, or systems whose resulting adaptations are distributed without chain-recorded provenance. It also does not reach systems treating federated learning purely as a privacy-engineering technique without the credentialing, admissibility, and lineage structure. The disclosure preserves room for evolution of privacy mechanisms, aggregation protocols, and federation topologies under the declared governance procedures, treating such evolution as the operation of the chain rather than departure from it.