433 citations · 1.3k across the 96 of their papers we have counts for
7 papers · 1 filter
Fully Distributed Online Training of Graph Neural Networks in Networked Systems
Rostyslav Olshevskyi, Zhongyuan Zhao, Kevin Chan +3
Graph neural networks (GNNs) are powerful tools for developing scalable, decentralized artificial intelligence in large-scale networked systems, such as wireless networks, power gr…
Ant Backpressure Routing for Wireless Multi-hop Networks with Mixed Traffic Patterns
Negar Erfaniantaghvayi, Zhongyuan Zhao, Kevin Chan +3
A mixture of streaming and short-lived traffic presents a common yet challenging scenario for Backpressure routing in wireless multi-hop networks. Although state-of-the-art shortes…
Resilience and Security of Deep Neural Networks Against Intentional and Unintentional Perturbations: Survey and Research Challenges
Sazzad Sayyed, Milin Zhang, Shahriar Rifat +3
In order to deploy deep neural networks (DNNs) in high-stakes scenarios, it is imperative that DNNs provide inference robust to external perturbations - both intentional and uninte…
Biased Backpressure Routing Using Link Features and Graph Neural Networks
Zhongyuan Zhao, Bojan Radojičić, Gunjan Verma +2
To reduce the latency of Backpressure (BP) routing in wireless multi-hop networks, we propose to enhance the existing shortest path-biased BP (SP-BP) and sojourn time-based backlog…
Plug-and-Play Transformer Modules for Test-Time Adaptation
Xiangyu Chang, Sk Miraj Ahmed, Srikanth V. Krishnamurthy +4
Parameter-efficient tuning (PET) methods such as LoRA, Adapter, and Visual Prompt Tuning (VPT) have found success in enabling adaptation to new domains by tuning small modules with…
FLASH: Federated Learning Across Simultaneous Heterogeneities
Xiangyu Chang, Sk Miraj Ahmed, Srikanth V. Krishnamurthy +4
The key premise of federated learning (FL) is to train ML models across a diverse set of data-owners (clients), without exchanging local data. An overarching challenge to this date…
Learning Non-myopic Power Allocation in Constrained Scenarios
Arindam Chowdhury, Santiago Paternain, Gunjan Verma +2
We propose a learning-based framework for efficient power allocation in ad hoc interference networks under episodic constraints. The problem of optimal power allocation -- for maxi…