在无法访问模型的封闭环境下，用轻量适配器实现个性化推理。
SALT: A Lightweight Model Adaptation Method for Closed Split Computing Environments
- 客户端部署小型可训练适配器，优化头部模型输出的特征。
- 在CIFAR-10/100上比微调准确率更高，训练延迟更低。
- 适合边缘AI中资源受限、模型私有且网络不稳定的场景。
我们提出SALT（Split-Adaptive Lightweight Tuning），一种面向封闭式分割计算环境的轻量级模型适配框架。在该环境中，头部与尾部网络均属专有且对用户不可见。传统适配方法因需访问参数或架构而失效。SALT通过在客户端引入一个紧凑的可训练适配器，对头部网络输出的隐含特征进行精炼，实现用户特定适应，无需修改原模型且不增加通信开销。我们在CIFAR-10和CIFAR-100上的用户特定分类任务中评估SALT，结果表明其相比微调方法具有更高的准确率和更低的训练延迟。此外，SALT支持在高丢包网络下实现鲁棒推理，这是边缘-云环境中的常见挑战。部署开销极低，为严格系统约束下的边缘AI个性化推理提供实用解决方案。
原文摘要 · Abstract (English)
We propose SALT (Split-Adaptive Lightweight Tuning), a lightweight model adaptation framework for Split Computing under closed constraints, where the head and tail networks are proprietary and inaccessible to users. In such closed environments, conventional adaptation methods are infeasible since they require access to model parameters or architectures. SALT addresses this challenge by introducing a compact, trainable adapter on the client side to refine latent features from the head network, enabling user-specific adaptation without modifying the original models or increasing communication overhead. We evaluate SALT on user-specific classification tasks with CIFAR-10 and CIFAR-100, demonstrating improved accuracy with lower training latency compared to fine-tuning methods. Furthermore, SALT facilitates model adaptation for robust inference over lossy networks, a common challenge in edge-cloud environments. With minimal deployment overhead, SALT offers a practical solution for personalized inference in edge AI systems under strict system constraints.
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