用零知识证明验证LoRA权重有效性，保护隐私且1秒内完成验证。
ZKLoRA: Efficient Zero-Knowledge Proofs for LoRA Verification
- 通过简洁证明和多方推理验证LoRA与基模型兼容性
- 单个LoRA模块验证仅需1-2秒，支持实时协作
- 适合分布式训练中保护权重隐私的场景
低秩适配（LoRA）是定制大模型的常用方法。在分布式、不可信的训练环境中，开源基模型用户可能需要使用外部贡献者提供的LoRA权重，这带来两个需求：(1) 用户必须确认该LoRA权重在搭配目标基模型时有效；(2) 贡献者需在获得报酬前保密其私有权重。我们提出ZKLoRA，一种基于简洁零知识证明和新型多方推理的验证协议，可在不暴露LoRA权重的情况下验证其与基模型的兼容性。ZKLoRA提供确定性正确性保障，在最先进的大语言模型上每个LoRA模块验证时间仅需1-2秒。该低延迟方案支持近乎实时验证，促进地理分散团队与基于合约的训练流程中的安全协作。协议确保交付的LoRA模块按声明工作，既保护了贡献者的知识产权，又为基模型用户提供兼容性与溯源验证。
原文摘要 · Abstract (English)
Low-Rank Adaptation (LoRA) is a widely adopted method for customizing large-scale language models. In distributed, untrusted training environments, an open source base model user may want to use LoRA weights created by an external contributor, leading to two requirements: (1) the base model user must confirm that the LoRA weights are effective when paired with the intended base model, and (2) the LoRA contributor must keep their proprietary weights private until compensation is assured. We present ZKLoRA, a zero-knowledge verification protocol that relies on succinct proofs and our novel Multi-Party Inference procedure to verify LoRA-base model compatibility without exposing LoRA weights. ZKLoRA produces deterministic correctness guarantees and validates each LoRA module in only 1-2 seconds on state-of-the-art large language models. This low-latency approach enables nearly real-time verification and promotes secure collaboration among geographically decentralized teams and contract-based training pipelines. The protocol ensures that the delivered LoRA module works as claimed, safeguarding the contributor's intellectual property while providing the base model user with verification of compatibility and lineage.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。