SALT: A Lightweight Model Adaptation Method for Closed Split Computing Environments

--We propose SAL T (Split-Adaptive Lightweight T un-ing), 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. SAL T 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 SAL T on user-specific classification tasks with CIF AR-10 and CIF AR-100, demonstrating improved accuracy with lower training latency compared to fine-tuning methods. With minimal deployment overhead, SAL T offers a practical solution for personalized inference in edge AI systems under strict system constraints. The increasing scale of deep learning models deployed in cloud-based AI services has raised concerns regarding server-side computational load and inference latency. To address these challenges, Split Computing has emerged as a promising paradigm that offloads part of a large cloud-based model to the client device [1], [2]. In this architecture, the neural network model is partitioned into a head network executed on the client and a tail network retained on the cloud.