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Found 7 papers, 1 papers with code
AutoReP: Automatic ReLU Replacement for Fast Private Network Inference
The growth of the Machine-Learning-As-A-Service (MLaaS) market has highlighted clients' data privacy and security issues.
RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inference
The proliferation of deep learning (DL) has led to the emergence of privacy and security concerns.
PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference
The rapid growth and deployment of deep learning (DL) has witnessed emerging privacy and security concerns.
A Secure and Efficient Federated Learning Framework for NLP
In this work, we consider the problem of designing secure and efficient federated learning (FL) frameworks.
SAPAG: A Self-Adaptive Privacy Attack From Gradients
Distributed learning such as federated learning or collaborative learning enables model training on decentralized data from users and only collects local gradients, where data is processed close to its sources for data privacy.
ESMFL: Efficient and Secure Models for Federated Learning
Nowadays, Deep Neural Networks are widely applied to various domains.
Against Membership Inference Attack: Pruning is All You Need
The large model size, high computational operations, and vulnerability against membership inference attack (MIA) have impeded deep learning or deep neural networks (DNNs) popularity, especially on mobile devices.
