Verifiable Fine-Tuning for LLMs: Zero-Knowledge Training Proofs Bound to Data Provenance and Policy
Existing parameter-efficient fine-tuning (PEFT) of large language models lacks verifiable guarantees regarding training data provenance, update integrity, and policy compliance. Method: We propose the first end-to-end verifiable PEFT framework, integrating zero-knowledge proofs (ZKPs), data commitment ledgers, verifiable sampling, PEFT-specific arithmetic circuits, semantic constraints for AdamW optimizer behavior, and recursive proof aggregation—executed within a trusted execution environment for millisecond-scale verification. Contribution/Results: Our framework is the first to jointly verify data provenance, sampling privacy, optimizer semantics, and policy quotas under ZKP. It supports federated learning and probabilistic auditing. Experiments on English and bilingual instruction datasets show no utility degradation, zero policy violations, negligible index leakage, and proof generation/verification overheads suitable for practical deployment.