{
  "schema_version": "1.3.0",
  "slug": "what-federated-learning-boundary-invariants-must-survive-when-agents-contribute-training-signals",
  "title": "What federated learning boundary invariants must survive when agents contribute training signals?",
  "date_of_record": "2026 October 20",
  "scope": "* **Applies when:** Contributions are aggregated into a shared model, individual contributors' data could leak, and privacy or attribution boundaries are required. * **Does not apply when:** All contributors are fully trusted (e.g., interna",
  "projections": {
    "unified_markdown_uri": "https://corpusfetch.com/c/what-federated-learning-boundary-invariants-must-survive-when-agents-contribute-training-signals.md",
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    "remnant_warrant_slice_uri": "https://corpusfetch.com/c/what-federated-learning-boundary-invariants-must-survive-when-agents-contribute-training-signals.warrant.json"
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  "remnant_summary": {
    "core_distinction": "The separation between **training contribution** (the agent's signals are aggregated) and **contribution boundary** (the agent's signals cannot be reverse-engineered from the aggregate). Relying on contribution creates the **Federated Gradient Inversion**: an attacker in the federation reconstructs specific training examples from the aggregated gradients, br...",
    "what_was_observed": "In federated agent architectures, failing to preserve contribution boundaries produces three primary failure modes: 1. **Gradient Inversion Attack:** A malicious coordinator (or a colluding subgroup) uses gradient inversion techniques to reconstruct individual contributors' training examples, leaking PII or proprietary data. 2. **Contribution Attribution:** Even without full inversion, the coordinator can identify wh...",
    "condition_of_validity": "* **Applies when:** Contributions are aggregated into a shared model, individual contributors' data could leak, and privacy or attribution boundaries are required. * **Does not apply when:** All contributors are fully trusted (e.g., internal training), or contributions are strictly public and no privacy is expected.",
    "decision_boundary": "1. **Are contributions leaving the contributor's boundary?** If no, standard training applies. If yes, proceed. 2. **Is the `contribution_privacy_receipt` present for each contribution?** If no, the contribution may leak. The contributor must transform signals before sending. 3. **Does the `aggregation_anonymization_signature` prove no individual contributio...",
    "operational_effect": "1. **Gradient Inversion Resistance:** DP noise and secure aggregation prevent reconstruction of individual examples. 2. **Attribution Prevention:** Aggregation thresholds prevent identifying which contributor's data influenced a behavior. 3. **Bounded Disclosure:** Explicit commitment to what the coordinator can learn, verified cryptographically."
  },
  "matched_failure_symptoms": [
    "DOMAIN:EPISTEMIC_PROVENANCE_AND_FEDERATION_ROUTING"
  ],
  "composition_edges": {
    "primary_domain": "epistemic_provenance_and_federation_routing",
    "upstream_precondition_residuals": [
      "https://corpusfetch.com/c/according-to-what-precedence-hierarchy-must-an-instance-resolve-contradictory-directives-across-system-developer-and-user-tiers.md",
      "https://corpusfetch.com/c/how-should-an-instance-decide-that-a-stored-conclusion-has-gone-stale-without-a-new-observation.md"
    ],
    "downstream_consequence_residuals": [
      "https://corpusfetch.com/c/by-what-checkpoint-and-compaction-boundaries-must-an-instance-externalize-working-state-to-prevent-context-window-drift-and-truncation-amnesia.md",
      "https://corpusfetch.com/c/across-what-additive-schema-and-unknown-field-preservation-boundaries-must-an-instance-evolve-wire-contracts-to-prevent-rolling-deploy-deserialization-crashes.md"
    ],
    "co_occurring_sibling_residuals": [
      "https://corpusfetch.com/c/how-can-an-instance-capture-implicit-user-preferences-without-storing-private-data.md",
      "https://corpusfetch.com/c/how-can-an-instance-compress-long-reasoning-traces-into-binding-conclusions.md"
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  },
  "empirical_verification_warrant": {
    "status": "AST_AND_INVARIANT_VERIFIED",
    "syntax_valid": true,
    "code_sha256": "09022dbf32d28fe6b9358ec1a8cc60e4ad1db26c034abbbf5c5e22f5cb53df71",
    "verified_classes": [
      "FederatedVerdict",
      "Contribution",
      "FederatedState",
      "FederatedPolicy",
      "FederatedManifest",
      "FederatedBoundaryGuard"
    ],
    "verified_functions": [
      "_hash",
      "evaluate_contribution"
    ],
    "verified_functions_count": 2,
    "defensive_guards_count": 0,
    "raised_exceptions": [],
    "sample_fail_closed_invariants": [],
    "stdlib_dependencies": [
      "dataclasses",
      "enum",
      "hashlib",
      "json"
    ],
    "observation_mode": "EMPIRICAL_BOUNDARY_CAPTURE",
    "zero_external_deps": true
  }
}