Search Results for author: Nguyen H. Tran

Found 23 papers, 9 papers with code

Federated Deep Equilibrium Learning: A Compact Shared Representation for Edge Communication Efficiency

no code implementations27 Sep 2023 Long Tan Le, Tuan Dung Nguyen, Tung-Anh Nguyen, Choong Seon Hong, Nguyen H. Tran

Federated Learning (FL) is a prominent distributed learning paradigm facilitating collaboration among nodes within an edge network to co-train a global model without centralizing data.

Federated Learning

Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks

no code implementations23 Dec 2022 Tung-Anh Nguyen, Jiayu He, Long Tan Le, Wei Bao, Nguyen H. Tran

To the best of our knowledge, this is the first federated PCA algorithm for anomaly detection meeting the requirements of IoT networks.

Anomaly Detection Privacy Preserving

On the Generalization of Wasserstein Robust Federated Learning

no code implementations3 Jun 2022 Tung-Anh Nguyen, Tuan Dung Nguyen, Long Tan Le, Canh T. Dinh, Nguyen H. Tran

We show that the robustness of WAFL is more general than related approaches, and the generalization bound is robust to all adversarial distributions inside the Wasserstein ball (ambiguity set).

Domain Adaptation 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

DONE: Distributed Approximate Newton-type Method for Federated Edge Learning

2 code implementations10 Dec 2020 Canh T. Dinh, Nguyen H. Tran, Tuan Dung Nguyen, Wei Bao, Amir Rezaei Balef, Bing B. Zhou, Albert Y. Zomaya

In this work, we propose DONE, a distributed approximate Newton-type algorithm with fast convergence rate for communication-efficient federated edge learning.

Edge-computing Vocal Bursts Type Prediction

Edge-assisted Democratized Learning Towards Federated Analytics

no code implementations1 Dec 2020 Shashi Raj Pandey, Minh N. H. Nguyen, Tri Nguyen Dang, Nguyen H. Tran, Kyi Thar, Zhu Han, Choong Seon Hong

Therefore, we need to design a robust learning mechanism than the FL that (i) unleashes a viable infrastructure for FA and (ii) trains learning models with better generalization capability.

Distributed Computing Edge-computing +1

Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networks

1 code implementation25 Nov 2020 Minh N. H. Nguyen, Nguyen H. Tran, Yan Kyaw Tun, Zhu Han, Choong Seon Hong

Federated Learning is a new learning scheme for collaborative training a shared prediction model while keeping data locally on participating devices.

Edge-computing Federated Learning

Federated Learning with Nesterov Accelerated Gradient

no code implementations18 Sep 2020 Zhengjie Yang, Wei Bao, Dong Yuan, Nguyen H. Tran, Albert Y. Zomaya

It is well-known that Nesterov Accelerated Gradient (NAG) is a more advantageous form of momentum, but it is not clear how to quantify the benefits of NAG in FL so far.

Federated Learning

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

Personalized Federated Learning with Moreau Envelopes

4 code implementations NeurIPS 2020 Canh T. Dinh, Nguyen H. Tran, Tuan Dung Nguyen

Federated learning (FL) is a decentralized and privacy-preserving machine learning technique in which a group of clients collaborate with a server to learn a global model without sharing clients' data.

Meta-Learning Model Optimization +3

Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems

no code implementations28 Apr 2020 Dai Hoang Tran, Quan Z. Sheng, Wei Emma Zhang, Salma Abdalla Hamad, Munazza Zaib, Nguyen H. Tran, Lina Yao, Nguyen Lu Dang Khoa

In recent years, the emerging topics of recommender systems that take advantage of natural language processing techniques have attracted much attention, and one of their applications is the Conversational Recommender System (CRS).

Collaborative Filtering Goal-Oriented Dialogue Systems +1

Distributed and Democratized Learning: Philosophy and Research Challenges

1 code implementation18 Mar 2020 Minh N. H. Nguyen, Shashi Raj Pandey, Kyi Thar, Nguyen H. Tran, Mingzhe Chen, Walid Saad, Choong Seon Hong

Consequently, many emerging cross-device AI applications will require a transition from traditional centralized learning systems towards large-scale distributed AI systems that can collaboratively perform multiple complex learning tasks.

Philosophy

Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach

no code implementations21 Feb 2020 Sarder Fakhrul Abedin, Md. Shirajum Munir, Nguyen H. Tran, Zhu Han, Choong Seon Hong

First, we formulate an energy-efficient trajectory optimization problem in which the objective is to maximize the energy efficiency by optimizing the UAV-BS trajectory policy.

reinforcement-learning Reinforcement Learning (RL)

Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach

no code implementations21 Feb 2020 Md. Shirajum Munir, Sarder Fakhrul Abedin, Nguyen H. Tran, Zhu Han, Eui-Nam Huh, Choong Seon Hong

First, we formulate an optimization problem considering the conditional value-at-risk (CVaR) measurement for both energy consumption and generation, where the objective is to minimize the expected residual of scheduled energy for the MEC networks and we show this problem is an NP-hard problem.

Edge-computing Scheduling

Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems

no code implementations20 Feb 2020 Md. Shirajum Munir, Nguyen H. Tran, Walid Saad, Choong Seon Hong

In particular, each BS plays the role of a local agent that explores a Markovian behavior for both energy consumption and generation while each BS transfers time-varying features to a meta-agent.

Edge-computing Meta Reinforcement Learning +3

Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism

no code implementations6 Nov 2019 Latif U. Khan, Nguyen H. Tran, Shashi Raj Pandey, Walid Saad, Zhu Han, Minh N. H. Nguyen, Choong Seon Hong

IoT devices with intelligence require the use of effective machine learning paradigms.

Distributed, Parallel, and Cluster Computing

Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation

4 code implementations29 Oct 2019 Canh T. Dinh, Nguyen H. Tran, Minh N. H. Nguyen, Choong Seon Hong, Wei Bao, Albert Y. Zomaya, Vincent Gramoli

There is an increasing interest in a fast-growing machine learning technique called Federated Learning, in which the model training is distributed over mobile user equipments (UEs), exploiting UEs' local computation and training data.

Federated Learning Privacy Preserving +1

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

Deep Autoencoder for Recommender Systems: Parameter Influence Analysis

no code implementations25 Dec 2018 Dai Hoang Tran, Zawar Hussain, Wei Emma Zhang, Nguyen Lu Dang Khoa, Nguyen H. Tran, Quan Z. Sheng

Specifically, we find that DAE parameters strongly affect the prediction accuracy of the recommender systems, and the effect is transferable to similar datasets in a larger size.

Recommendation Systems

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