The Art of Mixology: Mixup-based Obfuscation for Privacy-Preserving Split Learning in Large Language Models
arXiv:2606.16801v1 Announce Type: new Abstract: Split learning provides a practical paradigm for resource-constrained users to train Large Language Models (LLMs) by offloading computation-intensive layers to a server while keeping raw data local. However, existing privacy-preserving split learning methods still face a difficult trade-off among utility, privacy, efficiency, and stability. Specifically, these methods often suffer from substantial utility degradation, remain vulnerable to advanced data reconstruction attacks, incur prohibitive computational and communication overhead, or exhibit
The increasing complexity and resource demands of large language models are pushing innovations in distributed learning paradigms, while privacy concerns remain paramount.
This research addresses a critical trade-off in privacy-preserving distributed learning for LLMs, aiming to enable wider adoption by mitigating privacy risks without sacrificing utility or efficiency.
Better methods for privacy-preserving split learning could enable a broader range of enterprises and compute-constrained entities to train LLMs securely and efficiently, potentially decentralizing AI development.
- · Resource-constrained LLM developers
- · Privacy-focused AI companies
- · Distributed AI platforms
- · Cloud computing providers with privacy offerings
- · Centralized LLM training paradigms
- · Data-hungry AI methods without privacy mechanisms
Improved privacy in distributed LLM training will facilitate more widespread deployment and data utilization.
Increased accessibility to train powerful LLMs could accelerate AI development and customization for various industries.
Enhanced privacy could reduce legal and ethical barriers, fostering greater trust and public acceptance of advanced AI systems.
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