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papersSEP 10 04:00 UTC

arXiv paper proposes privacy-preserving split learning for federated LLM fine-tuning

A new arXiv paper addresses fine-tuning large language models in settings where participants cannot host the full model locally, often because the model owner keeps it proprietary. The authors introduce a split learning approach designed to preserve privacy during federated fine-tuning on domain-specific data.

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