activity
20172022
most citedAsynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise

17 citations · 25 across the 5 of their papers we have counts for

collaborators

12 papers

cs.CR2022

Secure Remote Attestation with Strong Key Insulation Guarantees

Deniz Gurevin, Chenglu Jin, Phuong Ha Nguyen +2

Recent years have witnessed a trend of secure processor design in both academia and industry. Secure processors with hardware-enforced isolation can be a solid foundation of cloud…

cs.LG2020

Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes

Marten van Dijk, Nhuong V. Nguyen, Toan N. Nguyen +3

Hogwild! implements asynchronous Stochastic Gradient Descent (SGD) where multiple threads in parallel access a common repository containing training data, perform SGD iterations an…

cs.LG202017 cited

Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise

Marten van Dijk, Nhuong V. Nguyen, Toan N. Nguyen +3

The feasibility of federated learning is highly constrained by the server-clients infrastructure in terms of network communication. Most newly launched smartphones and IoT devices…

cs.LG2020

A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning

Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan +3

We propose a novel hybrid stochastic policy gradient estimator by combining an unbiased policy gradient estimator, the REINFORCE estimator, with another biased one, an adapted SARA…

cs.LG2019

BUZz: BUffer Zones for defending adversarial examples in image classification

Kaleel Mahmood, Phuong Ha Nguyen, Lam M. Nguyen +2

We propose a novel defense against all existing gradient based adversarial attacks on deep neural networks for image classification problems. Our defense is based on a combination…

cs.LG2019

DTN: A Learning Rate Scheme with Convergence Rate of for SGD

Lam M. Nguyen, Phuong Ha Nguyen, Dzung T. Phan +2

This paper has some inconsistent results, i.e., we made some failed claims because we did some mistakes for using the test criterion for a series. Precisely, our claims on the conv…