On the Robustness of Split Learning against Adversarial Attacks. (arXiv:2307.07916v2 [cs.LG] UPDATED)

Split learning enables collaborative deep learning model training while
preserving data privacy and model security by avoiding direct sharing of raw
data and model details (i.e., sever and clients only hold partial sub-networks
and exchange intermediate computations). However, existing research has mainly
focused on examining its reliability for privacy protection, with little
investigation into model security. Specifically, by exploring full models,
attackers can launch adversarial attacks, and split learning can mitigate this
severe threat by only disclosing part of models …

adversarial adversarial attacks attacks clients data data privacy deep learning exchange model training networks partial privacy reliability research robustness security sharing split learning training