Edge Inference Manager

The manager handles the distribution and lifecycle of trained global models from the central Model Registry to the edge fleet. This includes:

• Canary Rollouts: Staged deployment to a subset of devices to monitor performance before full release.
• A/B Testing: Managing multiple model versions concurrently to compare metrics like accuracy or latency.
• Atomic Rollbacks: Rapid reversion to a previous stable model version if a new deployment fails or degrades.
• Version Stitching: Ensuring client SDKs and device runtimes are compatible with the deployed model artifact.

It manages the execution of inference tasks on devices, optimizing for resource constraints and latency requirements. Key capabilities include:

• Dynamic Batching: Grouping inference requests on-device to maximize hardware accelerator (e.g., NPU, GPU) utilization.
• Priority Queuing: Ensuring high-priority, latency-sensitive predictions (e.g., anomaly detection) are processed before background tasks.
• Conditional Execution: Triggering model inference based on specific sensor data thresholds or events to conserve power.
• Workload Offloading: Deciding, based on policy, whether to compute locally or offload to a nearby edge server or the cloud when local resources are saturated.

The manager collects real-time operational metrics from the edge to ensure inference service quality and inform model updates. Monitored data includes:

• Inference Latency: End-to-end prediction time, critical for real-time applications.
• Hardware Utilization: CPU, NPU, memory, and battery consumption per model version.
• Model Performance: Tracking prediction drift or accuracy decay by comparing inference outputs against ground truth labels where available.
• Device Health: Monitoring connectivity, storage, and thermal status to preemptively manage failing nodes. This telemetry feeds back into the Convergence Monitor and influences future Client Selection for training.

It interfaces with the device's local data pipeline to provide context-aware inference. This involves:

• Feature Engineering: Applying the same preprocessing transformations used during training to raw sensor or application data before inference.
• Context Window Management: For sequence models (e.g., LSTMs), maintaining the appropriate state or history of data points on the device.
• Secure Data Handling: Ensuring sensitive input data for inference is processed in a protected environment (e.g., a trusted execution environment) and not persisted unnecessarily.
• Result Caching: Storing frequent or recent inference results to serve identical subsequent requests without recomputing, reducing latency and power use.

The Edge Inference Manager is not a standalone system; it is a subordinate component to the Federated Learning Orchestrator. Key integration points are:

• Model Update Pipeline: Receiving new global models post-aggregation from the Central Aggregator via the Model Registry.
• Feedback Loop: Sending aggregated, anonymized inference performance and data distribution metrics back to the orchestrator to inform the next Federated Job. This closes the Continuous Learning loop.
• Policy Enforcement: Applying governance and compliance rules (e.g., data residency, model licensing) defined centrally by the Compliance Checker.
• Unified Observability: Streaming logs and metrics to the central Audit Logger and Resource Monitor for a holistic system view.

It enforces security protocols at the inference endpoint to protect the model and the data. This includes:

• Model Integrity Verification: Using cryptographic signatures to ensure deployed model artifacts have not been tampered with.
• Authenticated Inference APIs: Ensuring only authorized applications or services on the device can request predictions from the managed model.
• Inference Logging & Audit: Recording which data was processed by which model version for compliance with regulations like the EU AI Act.
• Adversarial Input Detection: Implementing basic filters or anomaly detectors to shield the model from evasion attacks designed to force incorrect predictions.

The central server-side platform that manages the entire federated learning lifecycle. It coordinates the training rounds, dispatches tasks to clients, and aggregates their updates. The Edge Inference Manager is a downstream component that receives the final trained model from the orchestrator's registry for deployment to the edge.

• Core Functions: Client selection, task scheduling, secure aggregation orchestration, and convergence monitoring.
• Relationship: The orchestrator produces the model; the Edge Inference Manager consumes and operationalizes it.

A versioned repository for storing and managing machine learning model artifacts and their metadata. In a federated system, it holds the global model checkpoints, client model versions, and associated performance metrics. The Edge Inference Manager queries the registry to pull specific model versions for deployment to target edge device fleets.

• Key Metadata: Model version, creation round, performance scores, and compatibility matrices.
• Integration Point: Serves as the source of truth for models moving from federated training to edge inference.

A component responsible for the promotion and distribution of validated models to production environments. In federated learning, it works in tandem with the Edge Inference Manager to handle canary deployments, A/B testing, and staged rollouts of new global models across heterogeneous edge device populations.

• Primary Tasks: Packaging models for target runtimes (e.g., TensorFlow Lite, ONNX Runtime), managing deployment manifests, and orchestrating rollbacks.
• Distinction: The Deployment Manager handles the logistical distribution; the Edge Inference Manager handles the local execution and lifecycle on the device.

The orchestrator module that maintains the inventory and state of all participating edge devices. It profiles device capabilities (compute, memory), manages authentication, and tracks availability. The Edge Inference Manager uses this profiling data to ensure the correct model variant (e.g., quantized, pruned) is deployed to each device based on its hardware constraints.

• Profiled Attributes: OS version, available RAM, supported neural processing units (NPUs), and network connectivity type.
• Critical for Inference: Ensures model-device compatibility to prevent runtime failures.

A service that centrally stores and disseminates runtime configurations for all system components. For edge inference, it provides the Edge Inference Manager with the necessary parameters: inference batch size, pre/post-processing logic, telemetry reporting frequency, and fallback behaviors in case of model failure or connectivity loss.

• Dynamic Updates: Allows for remote reconfiguration of inference behavior without redeploying the entire model.
• Ensures Consistency: Guarantees uniform inference execution policies across a fleet of devices.

The client-side library installed on edge devices that enables participation in federated learning. It typically includes a lightweight inference runtime that is managed by the Edge Inference Manager. The SDK provides the APIs for loading models, performing local prediction, and reporting inference metrics and data drift back to the central orchestrator.

• Dual Purpose: Used for both local training (federated learning) and local inference (edge prediction).
• Managed Component: The Edge Inference Manager controls the inference lifecycle of the SDK's runtime engine.