3 citations · 11 across the 19 of their papers we have counts for
22 papers
Physics-Informed Direction-of-Arrival Estimation Over Distributed Edge Devices
Nathan Tatsuta, Rajeev Sahay
Direction-of-arrival (DoA) estimation is a fundamental array processing task that has benefited substantially from deep learning. Deploying such methods using data distributed acro…
Spatiotemporal Link Formation Prediction in Social Learning Networks Using Graph Neural Networks
Ali Mohammadiasl, Bita Akram, Seyyedali Hosseinalipour +1
Social learning networks (SLNs) are graphical representations that capture student interactions within educational settings (e.g., a classroom), with nodes representing students an…
REVERB-FL: Server-Side Adversarial and Reserve-Enhanced Federated Learning for Robust Audio Classification
Sathwika Peechara, Rajeev Sahay
Federated learning (FL) enables a privacy-preserving training paradigm for audio classification but is highly sensitive to client heterogeneity and poisoning attacks, where adversa…
A Speculative GLRT-Backed Approach for Robust Deep Learning-Based Array Processing
Nian-Cin Wang, Rajeev Sahay
Deep learning (DL) has recently emerged as an efficient approach for array processing tasks such as signal detection and direction of arrival. However, DL models lack statistical g…
Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges
Kasra Borazjani, Naji Khosravan, Rajeev Sahay +2
Multi-modal multi-task (M3T) foundation models (FMs) have recently shown transformative potential in artificial intelligence, with emerging applications in education. However, thei…
Privacy-Preserving Distributed Link Predictions Among Peers in Online Classrooms Using Federated Learning
Anurata Prabha Hridi, Muntasir Hoq, Zhikai Gao +4
Social interactions among classroom peers, represented as social learning networks (SLNs), play a crucial role in enhancing learning outcomes. While SLN analysis has recently garne…
Robust Eavesdropping in the Presence of Adversarial Communications for RF Fingerprinting
Andrew Yuan, Rajeev Sahay
Deep learning is an effective approach for performing radio frequency (RF) fingerprinting, which aims to identify the transmitter corresponding to received RF signals. However, bey…
An Uncertainty Quantification Framework for Deep Learning-Based Automatic Modulation Classification
Huian Yang, Rajeev Sahay
Deep learning has been shown to be highly effective for automatic modulation classification (AMC), which is a pivotal technology for next-generation cognitive communications. Yet,…
FLAME: A Federated Learning Approach for Multi-Modal RF Fingerprinting
Kasra Borazjani, Kiarash Kianfar, Seyyedali Hosseinalipour +1
Authorization systems are increasingly relying on processing radio frequency (RF) waveforms at receivers to fingerprint (i.e., determine the identity of) the corresponding transmit…
The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions
Maryam Ebrahimi, Rajeev Sahay, Seyyedali Hosseinalipour +1
Research has increasingly explored the application of artificial intelligence (AI) and machine learning (ML) within the mental health domain to enhance both patient care and health…
Mitigating Evasion Attacks in Federated Learning-Based Signal Classifiers
Su Wang, Rajeev Sahay, Adam Piaseczny +1
Recent interest in leveraging federated learning (FL) for radio signal classification (SC) tasks has shown promise but FL-based SC remains susceptible to model poisoning adversaria…
How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers?
Su Wang, Rajeev Sahay, Christopher G. Brinton
There has been recent interest in leveraging federated learning (FL) for radio signal classification tasks. In FL, model parameters are periodically communicated from participating…
Predicting Learning Interactions in Social Learning Networks: A Deep Learning Enabled Approach
Rajeev Sahay, Serena Nicoll, Minjun Zhang +4
We consider the problem of predicting link formation in Social Learning Networks (SLN), a type of social network that forms when people learn from one another through structured in…
Defending Adversarial Attacks on Deep Learning Based Power Allocation in Massive MIMO Using Denoising Autoencoders
Rajeev Sahay, Minjun Zhang, David J. Love +1
Recent work has advocated for the use of deep learning to perform power allocation in the downlink of massive MIMO (maMIMO) networks. Yet, such deep learning models are vulnerable…
A Neural Network-Prepended GLRT Framework for Signal Detection Under Nonlinear Distortions
Rajeev Sahay, Swaroop Appadwedula, David J. Love +1
Many communications and sensing applications hinge on the detection of a signal in a noisy, interference-heavy environment. Signal processing theory yields techniques such as the g…
Mitigating Gradient-based Adversarial Attacks via Denoising and Compression
Rehana Mahfuz, Rajeev Sahay, Aly El Gamal
Gradient-based adversarial attacks on deep neural networks pose a serious threat, since they can be deployed by adding imperceptible perturbations to the test data of any network,…
A Deep Ensemble-based Wireless Receiver Architecture for Mitigating Adversarial Attacks in Automatic Modulation Classification
Rajeev Sahay, Christopher G. Brinton, David J. Love
Deep learning-based automatic modulation classification (AMC) models are susceptible to adversarial attacks. Such attacks inject specifically crafted wireless interference into tra…
Frequency-based Automated Modulation Classification in the Presence of Adversaries
Rajeev Sahay, Christopher G. Brinton, David J. Love
Automatic modulation classification (AMC) aims to improve the efficiency of crowded radio spectrums by automatically predicting the modulation constellation of wireless RF signals.…
Non-Intrusive Detection of Adversarial Deep Learning Attacks via Observer Networks
Kirthi Shankar Sivamani, Rajeev Sahay, Aly El Gamal
Recent studies have shown that deep learning models are vulnerable to specifically crafted adversarial inputs that are quasi-imperceptible to humans. In this letter, we propose a n…
Ensemble Noise Simulation to Handle Uncertainty about Gradient-based Adversarial Attacks
Rehana Mahfuz, Rajeev Sahay, Aly El Gamal
Gradient-based adversarial attacks on neural networks can be crafted in a variety of ways by varying either how the attack algorithm relies on the gradient, the network architectur…
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks
Rajeev Sahay, Rehana Mahfuz, Aly El Gamal
The reliance on deep learning algorithms has grown significantly in recent years. Yet, these models are highly vulnerable to adversarial attacks, which introduce visually impercept…
Combatting Adversarial Attacks through Denoising and Dimensionality Reduction: A Cascaded Autoencoder Approach
Rajeev Sahay, Rehana Mahfuz, Aly El Gamal
Machine Learning models are vulnerable to adversarial attacks that rely on perturbing the input data. This work proposes a novel strategy using Autoencoder Deep Neural Networks to…