Split Unlearning. (arXiv:2308.10422v2 [cs.CR] UPDATED)

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 …

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