3 citations · 11 across the 19 of their papers we have counts for
8 papers · 1 filter
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…