Parameter-Efficient Fine-Tuning/ PEFT

Definition

Parameter-Efficient Fine-Tuning is family of methods that customize large models by training a small fraction of parameters. In defense applications, it reduces cost and classification exposure when tuning models for specific missions or theaters. The hard part is hidden coupling to the base model and weak regression testing after adapter changes, especially when systems are deployed across contested links, coalition boundaries, and mixed human-machine teams. KhanBMS treats it as a way to ship theatre-specific intelligence as replaceable modules, tying the concept back to modular command, edge execution, and auditable authority.

Reference attributes

Layer

model adaptation family

Operational value

Reduces cost and classification exposure when tuning models for specific missions or theaters

Primary risk

Hidden coupling to the base model and weak regression testing after adapter changes

KhanBMS role

A way to ship theatre-specific intelligence as replaceable modules

Related terms

• Low-Rank Adaptation (LoRA)
  Fine-tuning technique that updates small rank-decomposition matrices instead of all model weights.
• Model Cards for Defense
  Documentation artifacts describing model purpose, training data, metrics, limits, and approved uses.
• MLOps for Defense (MLOps-D)
  Lifecycle practices for building, testing, approving, deploying, monitoring, and updating military AI.
• Secure Model Provenance
  Cryptographic and procedural evidence tracking where a model, adapter, dataset, or artifact came from.