99 citations · 188 across the 22 of their papers we have counts for
10 papers · 1 filter
Federated Hyperdimensional Computing for Resource-Constrained Industrial IoT
Nikita Zeulin, Olga Galinina, Nageen Himayat +1
In the Industrial Internet of Things (IIoT) systems, edge devices often operate under strict constraints in memory, compute capability, and wireless bandwidth. These limitations ch…
Large-Margin Hyperdimensional Computing: A Learning-Theoretical Perspective
Nikita Zeulin, Olga Galinina, Ravikumar Balakrishnan +2
Overparameterized machine learning (ML) methods such as neural networks may be prohibitively resource intensive for devices with limited computational capabilities. Hyperdimensiona…
Buffer-based Gradient Projection for Continual Federated Learning
Shenghong Dai, Jy-yong Sohn, Yicong Chen +5
Continual Federated Learning (CFL) is essential for enabling real-world applications where multiple decentralized clients adaptively learn from continuous data streams. A significa…
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…
Streaming Encoding Algorithms for Scalable Hyperdimensional Computing
Anthony Thomas, Behnam Khaleghi, Gopi Krishna Jha +5
Hyperdimensional computing (HDC) is a paradigm for data representation and learning originating in computational neuroscience. HDC represents data as high-dimensional, low-precisio…
Dynamic Network-Assisted D2D-Aided Coded Distributed Learning
Nikita Zeulin, Olga Galinina, Nageen Himayat +2
Today, various machine learning (ML) applications offer continuous data processing and real-time data analytics at the edge of a wireless network. Distributed real-time ML solution…
Coded Federated Learning
Sagar Dhakal, Saurav Prakash, Yair Yona +2
Federated learning is a method of training a global model from decentralized data distributed across client devices. Here, model parameters are computed locally by each client devi…