Search Results for author: Tung T. Vu

Found 6 papers, 2 papers with code

Serving Federated Learning and Non-Federated Learning Users: A Massive MIMO Approach

no code implementations17 May 2022 Muhammad Farooq, Tung T. Vu, Hien Quoc Ngo, Le-Nam Tran

We propose a communication scheme that serves the downlink of the non-FL users (UEs) and the uplink of FL UEs in each half of the frequency band.

Federated Learning

How Does Cell-Free Massive MIMO Support Multiple Federated Learning Groups?

no code implementations20 Jul 2021 Tung T. Vu, Hien Quoc Ngo, Thomas L. Marzetta, Michail Matthaiou

Federated learning (FL) has been considered as a promising learning framework for future machine learning systems due to its privacy preservation and communication efficiency.

Federated Learning

A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization

2 code implementations14 Feb 2021 Canh T. Dinh, Tung T. Vu, Nguyen H. Tran, Minh N. Dao, Hongyu Zhang

Non-Independent and Identically Distributed (non- IID) data distribution among clients is considered as the key factor that degrades the performance of federated learning (FL).

Few-Shot Learning Multi-Task Learning +1

Joint Resource Allocation to Minimize Execution Time of Federated Learning in Cell-Free Massive MIMO

1 code implementation4 Sep 2020 Tung T. Vu, Duy T. Ngo, Hien Quoc Ngo, Minh N. Dao, Nguyen H. Tran, Richard H. Middleton

We then develop a new algorithm that is proven to converge to the neighbourhood of the stationary points of the formulated problem.

Information Theory Information Theory

Cell-Free Massive MIMO for Wireless Federated Learning

no code implementations27 Sep 2019 Tung T. Vu, Duy T. Ngo, Nguyen H. Tran, Hien Quoc Ngo, Minh N. Dao, Richard H. Middleton

This paper proposes a novel scheme for cell-free massive multiple-input multiple-output (CFmMIMO) networks to support any federated learning (FL) framework.

Signal Processing Information Theory Information Theory

Particle Swarm Optimization for Weighted Sum Rate Maximization in MIMO Broadcast Channels

no code implementations4 Aug 2015 Tung T. Vu, Ha Hoang Kha, Trung Q. Duong, Nguyen-Son Vo

In order to maximize the weighted sum-rate (WSR) of the system subject to the transmitted power constraint, the design problem is to find the pre-coding matrices at BTS and the decoding matrices at MSs.

Stochastic Optimization

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