Federated Model Registry

Records the complete provenance of every model version, creating a cryptographically verifiable audit trail. This includes:

• Training node identity and institutional source
• Hyperparameters and architecture configuration
• Parent model hash and aggregation round
• Code commit hash of the training script This ensures any model deployed in a clinical setting can be traced back to its exact origin, satisfying FDA and CE mark regulatory requirements for medical device software.

Aggregates evaluation metrics from distributed test sets without centralizing protected health information. The registry orchestrates:

• Federated evaluation rounds where metrics are computed locally
• Stratified reporting across demographic subgroups to detect bias
• Statistical significance testing across institutional cohorts This allows a central governance board to compare model drift and accuracy across hospitals while maintaining strict data locality.

Manages the controlled release of new global model versions to production inference endpoints. The registry enforces:

• Shadow mode deployment where a new model runs silently alongside the incumbent
• Traffic splitting to route a percentage of inference requests to a candidate model
• Automated rollback triggers if performance degrades below a defined threshold This prevents a faulty federated update from impacting patient care across the entire network.

Implements a multi-stakeholder gating process before a model is promoted to clinical use. The registry supports:

• Role-based access control for reviewers, auditors, and approvers
• Digital signatures from institutional review boards
• Compliance tagging against specific regulatory frameworks like HIPAA and GDPR This formalizes the sign-off process across competing hospital systems, ensuring no single institution can unilaterally deploy a model.

Serves as the single source of truth for all model artifacts, including serialized weights, tokenizers, and preprocessing logic. The registry stores:

• Pointer references to encrypted model blobs in institution-local object storage
• Schema definitions for input and output tensors
• Environment specifications including container images and library versions This guarantees that any model can be exactly reproduced for an audit, even years after training.

Continuously monitors deployed models for statistical degradation by comparing live inference distributions against training baselines. The registry triggers alerts on:

• Data drift where input feature distributions shift
• Concept drift where the relationship between inputs and outputs changes
• Prediction churn where model outputs fluctuate significantly between versions This provides early warning when a model trained on aggregated knowledge begins to fail on a specific local population.

The immutable, cryptographically-verifiable record of a model's entire lifecycle within the registry. It tracks the exact data schema version, hyperparameters, aggregation algorithm (e.g., FedAvg), and privacy budget (ε) used to produce a specific model artifact. This allows auditors to trace a diagnostic prediction back to the precise training conditions without accessing the raw patient data, satisfying FDA's Predetermined Change Control Plan (PCCP) requirements for AI/ML-enabled medical devices.

A systematic protocol for assigning unique, semantically meaningful identifiers to model artifacts generated across different sites and training rounds. Unlike standard semantic versioning (MAJOR.MINOR.PATCH), federated versioning must encode global round number, aggregation timestamp, and participating node quorum. The registry enforces immutable staging (development, shadow, production) and automates the promotion of models only after they pass federated evaluation gates, preventing unverified models from reaching clinical deployment.

A structured, machine-readable transparency document stored in the registry that details a model's intended use, performance characteristics across demographic subgroups, evaluation results per institution, and known limitations. It adapts Google's Model Card Toolkit for a decentralized context, aggregating fairness metrics (e.g., equalized odds) from each silo without revealing site-specific data distributions. This serves as the primary artifact for algorithmic explainability audits.

The registry's integrated benchmarking subsystem that orchestrates the execution of standardized test suites across distributed nodes without centralizing validation data. It supports cross-institutional hold-out sets and federated confusion matrices to compute global precision, recall, and AUC-ROC. The registry enforces evaluation gates—a model cannot be promoted to production unless it surpasses a pre-defined threshold on all participating sites, mitigating the risk of site-specific performance degradation.

A cryptographic process where each participating institution digitally signs a model artifact's hash using its private key before registration. The registry collects these multi-party signatures to create a verifiable attestation that a specific model version was the exact output of an agreed-upon federated training round. This provides non-repudiation and ensures that no single party, including the central aggregation server, can tamper with the model weights after the fact.

An automated governance mechanism that allows the registry to instantly revert all inference endpoints in the network to a previous, stable model version. This is triggered by real-time performance monitoring detecting concept drift or a critical safety issue (e.g., a sudden drop in sensitivity for a specific condition). The rollback command propagates to all edge inference nodes, ensuring clinical operations continue uninterrupted with a validated, safe model while the problematic version is quarantined for forensic analysis.