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Found 3 papers, 3 papers with code
FedSIS: Federated Split Learning with Intermediate Representation Sampling for Privacy-preserving Generalized Face Presentation Attack Detection
In FedSIS, a hybrid Vision Transformer (ViT) architecture is learned using a combination of FL and split learning to achieve robustness against statistical heterogeneity in the client data distributions without any sharing of raw data (thereby preserving privacy).
FeSViBS: Federated Split Learning of Vision Transformer with Block Sampling
In this work, we propose a framework for medical imaging classification tasks called Federated Split learning of Vision transformer with Block Sampling (FeSViBS).
Suppressing Poisoning Attacks on Federated Learning for Medical Imaging
Federated Learning (FL) is a promising solution that enables collaborative training through exchange of model parameters instead of raw data.
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