Federated Model Registry

Automatically captures the complete provenance of every model artifact. The registry records the exact hyperparameters, training dataset hash, code commit, and federated aggregation round that produced a specific version.

• Links global models to their constituent local updates via cryptographic hashes.
• Enables full reproducibility for regulatory audits.
• Prevents model provenance spoofing by verifying the chain of custody from edge node to central aggregator.

Enforces a unified schema for describing models trained across heterogeneous hospital environments. The registry maps disparate local metadata into a Federated Common Data Model for models.

• Standardizes fields for training data distribution, evaluation metrics, and intended use.
• Facilitates semantic search across all registered models.
• Ensures that a model trained at Hospital A can be safely evaluated for deployment at Hospital B.

Implements granular, role-based permissions that govern who can register, stage, approve, or deploy models within the federated network. Integrates with existing enterprise identity providers.

• Defines approval gates requiring sign-off from clinical governance boards before a model moves to production.
• Supports air-gapped registries for highly sensitive environments.
• Logs every human and system interaction for an immutable audit trail.

Integrates with federated evaluation frameworks to automatically log model performance against canonical validation sets. The registry stores accuracy, AUC-ROC, F1 scores, and fairness metrics for each version.

• Compares new candidates against the current champion model in a champion/challenger paradigm.
• Flags model drift or performance regression before production deployment.
• Provides a dashboard for visualizing performance trends across all registered models.

Serves as the bridge between training and production. The registry stores the actual serialized model artifacts alongside their metadata and integrates with CI/CD pipelines and inference serving infrastructure.

• Supports canary deployments by routing a percentage of inference traffic to a newly registered model.
• Automates rollbacks to a previous stable version if performance degrades.
• Manages the lifecycle from experimental to staged to production to archived.

Generates a Model Card and a FactSheet for every registered model, documenting its development, evaluation, and ethical considerations. This is critical for HIPAA and FDA SaMD compliance.

• Auto-populates regulatory documentation with lineage and performance data.
• Tracks data use agreements and consent scope for each model's training data.
• Provides a single, auditable source of truth for demonstrating algorithmic accountability to regulators.

The framework of policies, audit trails, and versioning controls that ensures accountability throughout the federated model lifecycle. Key components include:

• Provenance tracking: Immutable records of which client contributed which update
• Access control: Role-based permissions for model submission and approval
• Compliance mapping: Alignment with HIPAA, GDPR, and EU AI Act requirements
• Lineage graphs: Visual representations of model parentage across training rounds

A cryptographic protocol ensuring the central server can only compute the sum of client model updates without inspecting any individual contribution. This prevents:

• Model inversion attacks that reconstruct training data from gradients
• Membership inference that determines if a patient's record was used
• Gradient leakage exposing sensitive clinical features Common implementations use Shamir's Secret Sharing and pairwise masking.

The tendency of locally trained models to drift apart from the global optimum due to statistical heterogeneity in non-IID client data distributions. In healthcare, this occurs when:

• A rural clinic has different patient demographics than an urban hospital
• One institution specializes in rare diseases with skewed feature distributions
• Imaging equipment from different manufacturers produces varying pixel intensities Mitigation strategies include proximal regularization and FedProx algorithms.

The process of auditing and validating model performance across distributed nodes without centralizing test data. Techniques include:

• Federated AUC-ROC computation: Aggregating receiver operating characteristic curves
• Distributed calibration: Ensuring probability estimates are consistent across sites
• Bias detection: Identifying performance disparities across demographic subgroups
• Federated explainability: Computing SHAP or LIME values without exposing patient records

A technique for embedding a verifiable, persistent identifier into a federated model to prove intellectual property ownership and detect unauthorized usage. Watermarks are:

• Black-box verifiable: Detectable through specific input-output behaviors
• White-box verifiable: Embedded directly in model weights
• Robust to aggregation: Survive multiple rounds of federated averaging Critical for healthcare consortia protecting proprietary diagnostic models.

The core privacy principle where raw training data remains physically stored and processed on the client's local infrastructure. Only model updates—gradients, weights, or logits—ever leave the institution. This principle:

• Satisfies data residency requirements under GDPR Article 48
• Maintains chain of custody for protected health information (PHI)
• Eliminates the need for Business Associate Agreements (BAAs) for data sharing
• Enables collaboration between competitors without exposing trade secrets