Search Results for author:
Found 8 papers, 3 papers with code
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.
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm
Conventional wisdom in pruning Transformer-based language models is that pruning reduces the model expressiveness and thus is more likely to underfit rather than overfit.
TAG: Gradient Attack on Transformer-based Language Models
In this paper, as the first attempt, we formulate the gradient attack problem on the Transformer-based language models and propose a gradient attack algorithm, TAG, to reconstruct the local training data.
Federated Learning
Cryptography and Security
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.
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.
DTWNet: a Dynamic Time Warping Network
Dynamic Time Warping (DTW) is widely used as a similarity measure in various domains.
AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters
Reducing the model redundancy is an important task to deploy complex deep learning models to resource-limited or time-sensitive devices.
NOVEL AND EFFECTIVE PARALLEL MIX-GENERATOR GENERATIVE ADVERSARIAL NETWORKS
In our model, we propose an adjustment component that collects all the generated data points from the generators, learns the boundary between each pair of generators, and provides error to separate the support of each of the generated distributions.
