no code implementations • 3 Mar 2024 • Tien-Dung Cao, Nguyen T. Vuong, Thai Q. Le, Hoang V. N. Dao, Tram Truong-Huu
In this paper, we design and develop Asyn2F, an Asynchronous Federated learning Framework with bidirectional model aggregation.
no code implementations • 7 Feb 2024 • Akshita Abrol, Purnima Murali Mohan, Tram Truong-Huu
We adopt the Deep Q-Learning technique to train the DGCNN model in the DRL framework without the need for a labeled training dataset, enabling the framework to quickly adapt to traffic dynamics.
1 code implementation • 21 Oct 2021 • Cuong V. Nguyen, Tien-Dung Cao, Tram Truong-Huu, Khanh N. Pham, Binh T. Nguyen
In this paper, we perform an empirical study on the impact of several loss functions on the performance of standard GAN models, Deep Convolutional Generative Adversarial Networks (DCGANs).
no code implementations • 22 Jan 2020 • Tien-Dung Cao, Tram Truong-Huu, Hien Tran, Khanh Tran
However, its deployment in practice has been hurdled by two issues: the privacy of data that has to be aggregated centrally for model training and high communication overhead due to transmission of a large amount of data usually geographically distributed.
no code implementations • 10 Dec 2018 • Srinikethan Madapuzi Srinivasan, Tram Truong-Huu, Mohan Gurusamy
In such networks, link faults may result in a link disconnection without immediate replacement or a link reconnection, e. g., a wireless node changes its access point.