research
∙
05/31/2023
On the Capacity of Secure K-user Product Computation over a Quantum MAC
Inspired by a recent study by Christensen and Popovski on secure 2-user ...
share
research
∙
05/04/2023
The Capacity of Classical Summation over a Quantum MAC with Arbitrarily Distributed Inputs and Entanglements
The Σ-QMAC problem is introduced, involving S servers, K classical (𝔽_d)...
research
∙
04/15/2023
N-Sum Box: An Abstraction for Linear Computation over Many-to-one Quantum Networks
Linear computations over quantum many-to-one communication networks offe...
research
∙
03/20/2023
FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System
Federated Learning (FL) enables machine learning model training on distr...
research
∙
11/13/2022
FedRule: Federated Rule Recommendation System with Graph Neural Networks
Much of the value that IoT (Internet-of-Things) devices bring to “smart”...
research
∙
09/15/2022
On the Generic Capacity of K-User Symmetric Linear Computation Broadcast
Linear computation broadcast (LCBC) refers to a setting with d dimension...
research
∙
06/20/2022
The Capacity of 3 User Linear Computation Broadcast
The K User Linear Computation Broadcast (LCBC) problem is comprised of d...
research
∙
05/24/2022
Faithful Explanations for Deep Graph Models
This paper studies faithful explanations for Graph Neural Networks (GNNs...
research
∙
01/28/2022
FedGCN: Convergence and Communication Tradeoffs in Federated Training of Graph Convolutional Networks
Distributed methods for training models on graph datasets have recently ...
research
∙
10/11/2021
GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs
Graph Convolutional Networks (GCNs) are a popular method from graph repr...
research
∙
12/16/2020
