1 citations · 2 across the 5 of their papers we have counts for
5 papers
Distributed Training of Large Graph Neural Networks with Variable Communication Rates
Juan Cervino, Md Asadullah Turja, Hesham Mostafa +2
Training Graph Neural Networks (GNNs) on large graphs presents unique challenges due to the large memory and computing requirements. Distributed GNN training, where the graph is pa…
Investigating the Adversarial Robustness of Density Estimation Using the Probability Flow ODE
Marius Arvinte, Cory Cornelius, Jason Martin +1
Beyond their impressive sampling capabilities, score-based diffusion models offer a powerful analysis tool in the form of unbiased density estimation of a query sample under the tr…
Resource-Efficient Federated Hyperdimensional Computing
Nikita Zeulin, Olga Galinina, Nageen Himayat +1
In conventional federated hyperdimensional computing (HDC), training larger models usually results in higher predictive performance but also requires more computational, communicat…
Multi-Task Model Personalization for Federated Supervised SVM in Heterogeneous Networks
Aleksei Ponomarenko-Timofeev, Olga Galinina, Ravikumar Balakrishnan +3
Federated systems enable collaborative training on highly heterogeneous data through model personalization, which can be facilitated by employing multi-task learning algorithms. Ho…
Optimal Dynamic Orchestration in NDN-based Computing Networks
Hao Feng, Yi Zhang, Srikathyayani Srikanteswara +4
Named Data Networking (NDN) offers promising advantages in deploying next-generation service applications over distributed computing networks. We consider the problem of dynamic or…