Split learning is emerging as a powerful approach to decentralized machine learning, but the urgent task of unlearning to address privacy issues presents significant challenges. Conventional methods of retraining from scratch or gradient ascending require all clients' involvement, incurring high computational and communication overhead, particularly in public networks where clients lack resources and may be reluctant to participate in unlearning processes they have no interest. In this short article, we propose \textsc{SplitWiper}, a new framework that integrates the concept of SISA to reduce retraining costs and ensures no interference between the unlearning client and others in public networks. Recognizing the inherent sharding in split learning, we first establish the SISA-based design of \textsc{SplitWiper}. This forms the premise for conceptualizing two unlearning strategies for label-sharing and non-label-sharing scenarios. This article represents an earlier edition, with extensive experiments being conducted for the forthcoming full version.

翻译：拆分学习正成为一种强大的去中心化机器学习方法，但为应对隐私问题而进行的遗忘任务带来了重大挑战。传统的从头重新训练或梯度上升方法需要所有客户端的参与，会产生高昂的计算和通信开销，尤其是在公共网络中，客户端资源有限且可能不愿参与其不感兴趣的遗忘过程。在这篇短文中，我们提出了\textsc{SplitWiper}，这是一个新框架，它融合了SISA的概念以降低重新训练成本，并确保在公共网络中遗忘客户端与其他客户端之间无干扰。我们认识到拆分学习中固有的分片特性，首先建立了基于SISA的\textsc{SplitWiper}设计，这构成了为标签共享和非标签共享场景设计两种遗忘策略的前提。本文为早期版本，后续完整版本将开展广泛实验。