Search Results for author:
Found 6 papers, 2 papers with code
Serving Federated Learning and Non-Federated Learning Users: A Massive MIMO Approach
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.
How Does Cell-Free Massive MIMO Support Multiple Federated Learning Groups?
Federated learning (FL) has been considered as a promising learning framework for future machine learning systems due to its privacy preservation and communication efficiency.
A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization
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).
Joint Resource Allocation to Minimize Execution Time of Federated Learning in Cell-Free Massive MIMO
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
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
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.
