Model Registry

The registry serves as the single source of truth for all model binaries and metadata generated during federated training. This includes:

• Global Model Versions: Each aggregated model from a training round.
• Client Model Snapshots: Checkpoints or final updates from participating edge devices.
• Model Metadata: Hyperparameters, training configurations, data schemas, and performance metrics linked to each artifact. By centralizing storage, it eliminates artifact sprawl and ensures reproducibility across distributed clients.

It implements immutable version control for every model artifact, similar to Git for code. Key functions include:

• Unique Version IDs: Automatically generated hashes or sequential tags for each model checkpoint.
• Provenance Tracking: Recording the exact federated job, participating clients, and aggregation algorithm used to create a model version.
• Lineage Graphs: Visualizing the evolution from initial model to final deployed version, including all intermediate aggregation steps. This is critical for debugging, audit trails, and understanding model drift.

The registry governs the stage-based lifecycle of federated models, enforcing governance gates. Standard stages include:

• Staging: Newly aggregated models awaiting validation.
• Validation: Models undergoing performance and fairness evaluation on holdout data.
• Production: Approved models released for inference.
• Archived: Deprecated models retained for compliance. Automated promotion policies can trigger transitions based on metric thresholds, enabling continuous integration/deployment (CI/CD) for federated learning.

Beyond binaries, the registry indexes rich, queryable metadata for discovery and analysis. This encompasses:

• Performance Metrics: Accuracy, loss, and fairness scores per round and per client cohort.
• System Telemetry: Training duration, client dropout rates, and communication costs.
• Data Descriptors: Statistical summaries (e.g., data distribution sketches) from clients, respecting privacy. Engineers use this catalog to search and compare models by attributes (e.g., "models with >95% accuracy trained on >1000 clients") to select the best candidate for deployment.

The registry acts as the distribution hub for deploying approved global models back to the edge. Functions include:

• Model Packaging: Bundling the model with necessary inference runtimes and dependencies for heterogeneous client environments.
• Rollout Strategies: Managing canary deployments or A/B testing by serving different model versions to subsets of devices.
• Integration with Edge Inference Managers: Providing APIs for edge devices to pull the latest approved model version for local prediction, completing the federated learning loop.

It enforces role-based access control (RBAC) and maintains detailed audit logs for regulatory compliance (e.g., EU AI Act, HIPAA). This involves:

• Granular Permissions: Controlling who can read, write, or promote models.
• Immutable Audit Logs: Recording every action—model registration, download, promotion—with user, timestamp, and reason.
• Compliance Tagging: Associating models with privacy budgets (e.g., epsilon spent in differential privacy) or data residency requirements. This ensures the federated process is transparent and accountable.

The central software platform that manages the entire federated learning lifecycle. It coordinates the Round Coordinator, Client Manager, and Model Registry to execute training jobs across distributed devices. Its primary functions include:

• Job Scheduling: Defining and launching federated training tasks.
• Client Coordination: Managing device registration, selection, and communication.
• Aggregation Management: Orchestrating the secure combination of model updates.
• Lifecycle Automation: Sequencing model training, validation, and deployment workflows.

The server-side algorithm responsible for combining client model updates to produce a new global model. It is the computational core of the federated averaging process. Key mechanisms include:

• Update Aggregation: Applying algorithms like Federated Averaging (FedAvg) to compute a weighted average of client gradients or weights.
• Secure Aggregation Integration: Working with cryptographic protocols to sum updates without inspecting individual contributions.
• Model Delta Application: Merging the aggregated update with the previous global model version, which is then stored in the Model Registry.

The orchestrator module that handles the identity and state of all participating edge devices. It maintains a live inventory of clients for the Round Coordinator to select from. Its responsibilities are:

• Device Registration & Profiling: Cataloging client capabilities (compute, memory, network).
• Authentication & Authorization: Verifying device identity before allowing participation.
• Health & Availability Monitoring: Tracking which clients are online and ready for training.
• Lifecycle State Management: Updating client status (e.g., idle, training, uploading).

The component that executes a single federated learning round. It interacts directly with the Model Registry to fetch the current global model and the Client Manager to select participants. A round's phases are:

1. Client Selection: Choosing a subset of available devices using the Client Selection Module.
2. Task Dispatch: Sending the current model checkpoint and training configuration to selected clients.
3. Update Collection: Receiving and validating model updates from clients.
4. Aggregation Triggering: Sending collected updates to the Central Aggregator and storing the new model in the Model Registry.

The automation layer that sequences the multi-step federated learning pipeline. It defines the Directed Acyclic Graph (DAG) of tasks that constitute a Federated Job. It orchestrates interactions between:

• Model Registry: For retrieving initial models and storing outputs.
• Round Coordinator: To execute iterative training loops.
• Convergence Monitor: To evaluate if stopping criteria are met.
• Deployment Manager: To promote a validated model to production. This engine ensures reproducible, automated execution of the entire training-to-deployment lifecycle.

The component responsible for putting trained models into production. It works in tandem with the Model Registry to manage the promotion and distribution of model versions. Its core functions include:

• Model Promotion: Moving a model artifact from a staging environment to a production channel in the registry.
• Canary & A/B Testing: Gradually rolling out new model versions to subsets of edge devices or inference endpoints.
• Rollback Procedures: Reverting to a previous stable model version in case of performance regression.
• Edge Inference Coordination: Working with the Edge Inference Manager to push models to client devices for local prediction.