16 citations · 26 across the 14 of their papers we have counts for
4 papers · 1 filter
Energy-Efficient Massive MIMO for Federated Learning: Transmission Designs and Resource Allocations
Tung T. Vu, Hien Q. Ngo, Minh N. Dao +3
This work proposes novel synchronous, asynchronous, and session-based designs for energy-efficient massive multiple-input multiple-output networks to support federated learning (FL…
Energy-Efficient Massive MIMO for Serving Multiple Federated Learning Groups
Tung T. Vu, Hien Quoc Ngo, Duy T. Ngo +2
With its privacy preservation and communication efficiency, federated learning (FL) has emerged as a learning framework that suits beyond 5G and towards 6G systems. This work looks…
How Does Cell-Free Massive MIMO Support Multiple Federated Learning Groups?
Tung T. Vu, Hien Quoc Ngo, Thomas L. Marzetta +1
Federated learning (FL) has been considered as a promising learning framework for future machine learning systems due to its privacy preservation and communication efficiency. In b…
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…