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Life sciences · Preprint
arXiv · September 9, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint proposes a learned obfuscate-and-recover scheme to mitigate privacy leakage when training large language models collaboratively across institutions using split and federated learning. The authors demonstrate feasibility with modest utility loss and system overhead but provide no peer review, no comparison to existing defenses, and limited detail on experimental scale or generalizability.
Proof-of-concept with experiments. Participants in federated learning settings with privacy requirements who wish to fine-tune large language models without holding the complete model locally or sharing raw data.. Intervention: Learned obfuscate-and-recover scheme for privacy protection in federated split learning of LLMs.
Approach achieves strong privacy protection with modest utility loss and system overhead Addresses fundamental privacy paradox in autoregressive LLM fine-tuning where transmitted activations leak input data Existing perturbation-based defenses are reported as fundamentally ineffective in this setting
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The source did not state who this applies to in practice.
An early-stage technical study presenting a novel privacy-preserving method for federated LLM fine-tuning with experimental validation but no peer review, clinical outcomes, or comparative benchmarking against established standards.
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Fine-tuning large language models (LLMs) on domain-specific data is essential for downstream adaptation. In many deployments, a participant cannot hold the complete model locally. This happens because the model owner keeps the full model proprietary, or because the participant lacks sufficient compute resources. Split Learning (SL) addresses this by partitioning the model between the participant and a server so that only a small portion runs locally. When the underlying data is additionally distributed across multiple institutions with privacy requirements, Federated Learning (FL) further enables collaborative training across participants by sharing only model updates instead of raw data. In this combined setting, each client transmits intermediate activations to the server, and for LLM fine-tuning, this exchange poses an inherent privacy paradox. The autoregressive nature of LLMs causes the transmitted activations to leak the input, and existing perturbation-based defenses are fundamentally ineffective in this setting. We address this leakage through a learned obfuscate-and-recover scheme that protects participants' private datasets while still allowing an independently deployable model to be trained on the server side. Experiments demonstrate that our approach achieves strong privacy protection with modest utility loss and system overhead, making split-based federated LLM fine-tuning practically viable.
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