PPHH-VFL splits the model head into a plaintext public part secured by adversarial training and a small MPC private part, yielding up to 6 orders of magnitude faster inference than end-to-end MPC on models up to 86M parameters.
Overlearning reveals sensitive attributes
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FLRSP enhances privacy in federated learning by randomly selecting model parameters for sharing, delivering competitive image classification accuracy and improved resistance to reconstruction attacks on ResNet34 and ViT models using FedSGD and FedAvg.
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Private Vertical Federated Inference for Time-Series
PPHH-VFL splits the model head into a plaintext public part secured by adversarial training and a small MPC private part, yielding up to 6 orders of magnitude faster inference than end-to-end MPC on models up to 86M parameters.
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FLRSP: Privacy-Preserving Federated Learning Using Randomly Selected Model Parameters
FLRSP enhances privacy in federated learning by randomly selecting model parameters for sharing, delivering competitive image classification accuracy and improved resistance to reconstruction attacks on ResNet34 and ViT models using FedSGD and FedAvg.