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.
Federated machine learning: Concept and applications.ACM Transactions on Intelligent Systems and Technology (TIST), 10(2):1–19
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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.