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

17 citations · 19 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2021

AET-SGD: Asynchronous Event-triggered Stochastic Gradient Descent

Nhuong Nguyen, Song Han

Communication cost is the main bottleneck for the design of effective distributed learning algorithms. Recently, event-triggered techniques have been proposed to reduce the exchang…

cs.LG2021

Distributed Learning and its Application for Time-Series Prediction

Nhuong V. Nguyen, Sybille Legitime

Extreme events are occurrences whose magnitude and potential cause extensive damage on people, infrastructure, and the environment. Motivated by the extreme nature of the current g…

cs.LG2021

Proactive DP: A Multple Target Optimization Framework for DP-SGD

Marten van Dijk, Nhuong V. Nguyen, Toan N. Nguyen +2

We introduce a multiple target optimization framework for DP-SGD referred to as pro-active DP. In contrast to traditional DP accountants, which are used to track the expenditure of…

cs.LG2020★ 2 cited

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.LG2020★ 17 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…