On-Device Fine-Tuning

Definition

On-Device Fine-Tuning is local adaptation of AI models on tactical devices using recent mission or environment data. In defense applications, it helps models adjust to terrain, weather, unit style, or local emitter conditions. The hard part is catastrophic forgetting, data leakage, and unsafe unsupervised adaptation, especially when systems are deployed across contested links, coalition boundaries, and mixed human-machine teams. KhanBMS treats it as a capability KhanBMS gates behind signed policies and rollback controls, tying the concept back to modular command, edge execution, and auditable authority.

Reference attributes

Layer

edge adaptation technique

Operational value

Helps models adjust to terrain, weather, unit style, or local emitter conditions

Primary risk

Catastrophic forgetting, data leakage, and unsafe unsupervised adaptation

KhanBMS role

A capability KhanBMS gates behind signed policies and rollback controls

Related terms

• Edge Inference
  Running AI models on tactical hardware at the point of sensing or action instead of relying on distant cloud compute.
• Federated Learning (FL)
  Training approach where nodes learn from local data and share updates instead of raw data.
• Edge Model Registry
  Versioned catalog that tracks which models, adapters, signatures, and policies are deployed to tactical nodes.
• Model Observability
  Monitoring of model inputs, outputs, drift, latency, confidence, and failures after deployment.