  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.
