PFedQFT: Personalized Federated Quantum-Inspired Fine-Tuning for Medical Large Language Models
We propose PFedQFT, a quantum-inspired framework for personalized federated fine-tuning with a compact shared adapter.
ProofPaper ↗
Key points
- Medical data remain fragmented across institutional silos because of privacy, governance, and ownership constraints.
- Federated learning enables medical institutions to adapt large language models (LLMs) collaboratively without transferring raw records.
- However, independently parameterized low-rank adapters incur repeated parameter and communication costs across Transformer projections, while uniform aggregation may limit adaptation to heterogeneous institutional data.
- Specifically, PFedQFT constructs reusable orthonormal adaptation bases from compact skew-symmetric generators using a truncated matrix-exponential approximation followed by QR orthonormalization.
Sources (1)
- [1]PFedQFT: Personalized Federated Quantum-Inspired Fine-Tuning for Medical Large Language ModelsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 03:01 AM
We propose PFedQFT, a quantum-inspired framework for personalized federated fine-tuning with a compact shared adapter.
Medical data remain fragmented across institutional silos because of privacy, governance, and ownership constraints.
Extractive summary: sentences quoted from the sources.
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