99 citations · 188 across the 22 of their papers we have counts for
26 papers
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…
LATTEO: A Framework to Support Learning Asynchronously Tempered with Trusted Execution and Obfuscation
Abhinav Kumar, George Torres, Noah Guzinski +6
The privacy vulnerabilities of the federated learning (FL) paradigm, primarily caused by gradient leakage, have prompted the development of various defensive measures. Nonetheless,…
Soft Token Attacks Cannot Reliably Audit Unlearning in Large Language Models
Haokun Chen, Sebastian Szyller, Weilin Xu +1
Large language models (LLMs) are trained using massive datasets, which often contain undesirable content such as harmful texts, personal information, and copyrighted material. To a…
Imperceptible Adversarial Examples in the Physical World
Weilin Xu, Sebastian Szyller, Cory Cornelius +5
Adversarial examples in the digital domain against deep learning-based computer vision models allow for perturbations that are imperceptible to human eyes. However, producing simil…
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…
Enhancing O-RAN Security: Evasion Attacks and Robust Defenses for Graph Reinforcement Learning-based Connection Management
Ravikumar Balakrishnan, Marius Arvinte, Nageen Himayat +2
Adversarial machine learning, focused on studying various attacks and defenses on machine learning (ML) models, is rapidly gaining importance as ML is increasingly being adopted fo…
FastSample: Accelerating Distributed Graph Neural Network Training for Billion-Scale Graphs
Hesham Mostafa, Adam Grabowski, Md Asadullah Turja +3
Training Graph Neural Networks(GNNs) on a large monolithic graph presents unique challenges as the graph cannot fit within a single machine and it cannot be decomposed into smaller…
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…
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…
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…
6G: Connectivity in the Era of Distributed Intelligence
Shilpa Talwar, Nageen Himayat, Hosein Nikopour +3
The confluence of 5G and AI is transforming wireless networks to deliver diverse services at the Edge, driving towards a vision of pervasive distributed intelligence. Future 6G net…
Coded Computing for Low-Latency Federated Learning over Wireless Edge Networks
Saurav Prakash, Sagar Dhakal, Mustafa Akdeniz +4
Federated learning enables training a global model from data located at the client nodes, without data sharing and moving client data to a centralized server. Performance of federa…
Coded Computing for Federated Learning at the Edge
Saurav Prakash, Sagar Dhakal, Mustafa Akdeniz +2
Federated Learning (FL) is an exciting new paradigm that enables training a global model from data generated locally at the client nodes, without moving client data to a centralize…
Handling Spontaneous Traffic Variations in 5G+ via Offloading onto mmWave-Capable UAV `Bridges'
Nikita Tafintsev, Dmitri Moltchanov, Sergey Andreev +4
Unmanned aerial vehicles (UAVs) are increasingly employed for numerous public and civil applications, such as goods delivery, medicine, surveillance, and telecommunications. For th…
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…
Aerial Access and Backhaul in mmWave B5G Systems: Performance Dynamics and Optimization
Nikita Tafintsev, Dmitri Moltchanov, Mikhail Gerasimenko +7
The use of unmanned aerial vehicle (UAV)-based communication in millimeter-wave (mmWave) frequencies to provide on-demand radio access is a promising approach to improve capacity a…
Control Aware Radio Resource Allocation in Low Latency Wireless Control Systems
Mark Eisen, Mohammad M. Rashid, Konstantinos Gatsis +3
We consider the problem of allocating radio resources over wireless communication links to control a series of independent wireless control systems. Low-latency transmissions are n…
On the Temporal Effects of Mobile Blockers in Urban Millimeter-Wave Cellular Scenarios
Margarita Gapeyenko, Andrey Samuylov, Mikhail Gerasimenko +7
Millimeter-wave (mmWave) propagation is known to be severely affected by the blockage of the line-of-sight (LoS) path. In contrast to microwave systems, at shorter mmWave wavelengt…
Analysis of Human-Body Blockage in Urban Millimeter-Wave Cellular Communications
Margarita Gapeyenko, Andrey Samuylov, Mikhail Gerasimenko +7
The use of extremely high frequency (EHF) or millimeter-wave (mmWave) band has attracted significant attention for the next generation wireless access networks. As demonstrated by…