4 citations · 16 across the 17 of their papers we have counts for
4 papers · 1 filter
Key Focus Areas and Enabling Technologies for 6G
Christopher G. Brinton, Mung Chiang, Kwang Taik Kim +14
We provide a taxonomy of a dozen enabling network architectures, protocols, and technologies that will define the evolution from 5G to 6G. These technologies span the network proto…
Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks
Su Wang, Roberto Morabito, Seyyedali Hosseinalipour +2
The conventional federated learning (FedL) architecture distributes machine learning (ML) across worker devices by having them train local models that are periodically aggregated b…
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation
Su Wang, Mengyuan Lee, Seyyedali Hosseinalipour +3
The conventional federated learning (FedL) architecture distributes machine learning (ML) across worker devices by having them train local models that are periodically aggregated b…
Joint Optimization of Signal Design and Resource Allocation in Wireless D2D Edge Computing
Junghoon Kim, Taejoon Kim, Morteza Hashemi +2
In this paper, we study the distributed computational capabilities of device-to-device (D2D) networks. A key characteristic of D2D networks is that their topologies are reconfigura…