409 citations · 1k across the 16 of their papers we have counts for
18 papers
Convergence Analysis of Aggregation-Broadcast in LoRA-enabled Distributed Fine-Tuning
Xin Chen, Shuaijun Chen, Omid Tavallaie +3
Federated Learning (FL) enables collaborative model training across decentralized data sources while preserving data privacy. However, the growing size of Machine Learning (ML) mod…
Federated Deep Reinforcement Learning-based Bitrate Adaptation for Dynamic Adaptive Streaming over HTTP
Phuong L. Vo, Nghia T. Nguyen, Long Luu +3
In video streaming over HTTP, the bitrate adaptation selects the quality of video chunks depending on the current network condition. Some previous works have applied deep reinforce…
On the Generalization of Wasserstein Robust Federated Learning
Tung-Anh Nguyen, Tuan Dung Nguyen, Long Tan Le +2
In federated learning, participating clients typically possess non-i.i.d. data, posing a significant challenge to generalization to unseen distributions. To address this, we propos…
Self-Driving Cars and Driver Alertness
Nguyen H Tran, Abhaya C Nayak
Recent years have seen growing interest in the development of self-driving vehicles that promise (or threaten) to replace human drivers with intelligent software. However, current…
A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization
Canh T. Dinh, Tung T. Vu, Nguyen H. Tran +2
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). Sev…
DONE: Distributed Approximate Newton-type Method for Federated Edge Learning
Canh T. Dinh, Nguyen H. Tran, Tuan Dung Nguyen +4
There is growing interest in applying distributed machine learning to edge computing, forming federated edge learning. Federated edge learning faces non-i.i.d. and heterogeneous da…
Edge-assisted Democratized Learning Towards Federated Analytics
Shashi Raj Pandey, Minh N. H. Nguyen, Tri Nguyen Dang +4
A recent take towards Federated Analytics (FA), which allows analytical insights of distributed datasets, reuses the Federated Learning (FL) infrastructure to evaluate the summary…
Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networks
Minh N. H. Nguyen, Nguyen H. Tran, Yan Kyaw Tun +2
Federated Learning is a new learning scheme for collaborative training a shared prediction model while keeping data locally on participating devices. In this paper, we study a new…
An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approach
Tra Huong Thi Le, Nguyen H. Tran, Yan Kyaw Tun +4
Federated Learning (FL) is a distributed learning framework that can deal with the distributed issue in machine learning and still guarantee high learning performance. However, it…
Joint Resource Allocation to Minimize Execution Time of Federated Learning in Cell-Free Massive MIMO
Tung T. Vu, Duy T. Ngo, Hien Quoc Ngo +3
Due to its communication efficiency and privacy-preserving capability, federated learning (FL) has emerged as a promising framework for machine learning in 5G-and-beyond wireless n…
Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systems
Minh N. H. Nguyen, Shashi Raj Pandey, Tri Nguyen Dang +4
Emerging cross-device artificial intelligence (AI) applications require a transition from conventional centralized learning systems towards large-scale distributed AI systems that…
Personalized Federated Learning with Moreau Envelopes
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 s…
Ruin Theory for Energy-Efficient Resource Allocation in UAV-assisted Cellular Networks
Aunas Manzoor, Kitae Kim, Shashi Raj Pandey +4
Unmanned aerial vehicles (UAVs) can provide an effective solution for improving the coverage, capacity, and the overall performance of terrestrial wireless cellular networks. In pa…
Coexistence Mechanism between eMBB and uRLLC in 5G Wireless Networks
Anupam Kumar Bairagi, Md. Shirajum Munir, Madyan Alsenwi +5
uRLLC and eMBB are two influential services of the emerging 5G cellular network. Latency and reliability are major concerns for uRLLC applications, whereas eMBB services claim for…
Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approach
Madyan Alsenwi, Nguyen H. Tran, Mehdi Bennis +3
In this paper, we study the resource slicing problem in a dynamic multiplexing scenario of two distinct 5G services, namely Ultra-Reliable Low Latency Communications (URLLC) and en…
Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems
Md. Shirajum Munir, Nguyen H. Tran, Walid Saad +1
The stringent requirements of mobile edge computing (MEC) applications and functions fathom the high capacity and dense deployment of MEC hosts to the upcoming wireless networks. H…
A Crowdsourcing Framework for On-Device Federated Learning
Shashi Raj Pandey, Nguyen H. Tran, Mehdi Bennis +3
Federated learning (FL) rests on the notion of training a global model in a decentralized manner. Under this setting, mobile devices perform computations on their local data before…
Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation
Canh T. Dinh, Nguyen H. Tran, Minh N. H. Nguyen +4
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)…