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ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning
[article]
2022
arXiv
pre-print
This work aims to tackle Model Inversion (MI) attack on Split Federated Learning (SFL). SFL is a recent distributed training scheme where multiple clients send intermediate activations (i.e., feature map), instead of raw data, to a central server. While such a scheme helps reduce the computational load at the client end, it opens itself to reconstruction of raw data from intermediate activation by the server. Existing works on protecting SFL only consider inference and do not handle attacks
arXiv:2205.04007v1
fatcat:jzgxgfpclnan3oryd76vobotcu