92 citations · 224 across the 14 of their papers we have counts for
14 papers
Rebellion: Noise-Robust Reasoning Training for Audio Reasoning Models
Tiansheng Huang, Virat Shejwalkar, Oscar Chang +2
Instilling reasoning capabilities in large models (LMs) using reasoning training (RT) significantly improves LMs' performances. Thus Audio Reasoning Models (ARMs), i.e., audio LMs…
Multilingual and Multi-Accent Jailbreaking of Audio LLMs
Jaechul Roh, Virat Shejwalkar, Amir Houmansadr
Large Audio Language Models (LALMs) have significantly advanced audio understanding but introduce critical security risks, particularly through audio jailbreaks. While prior work h…
Decoding FL Defenses: Systemization, Pitfalls, and Remedies
Momin Ahmad Khan, Virat Shejwalkar, Yasra Chandio +2
While the community has designed various defenses to counter the threat of poisoning attacks in Federated Learning (FL), there are no guidelines for evaluating these defenses. Thes…
Security Analysis of SplitFed Learning
Momin Ahmad Khan, Virat Shejwalkar, Amir Houmansadr +1
Split Learning (SL) and Federated Learning (FL) are two prominent distributed collaborative learning techniques that maintain data privacy by allowing clients to never share their…
The Perils of Learning From Unlabeled Data: Backdoor Attacks on Semi-supervised Learning
Virat Shejwalkar, Lingjuan Lyu, Amir Houmansadr
Semi-supervised machine learning (SSL) is gaining popularity as it reduces the cost of training ML models. It does so by using very small amounts of (expensive, well-inspected) lab…
Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints
Virat Shejwalkar, Arun Ganesh, Rajiv Mathews +5
In this work, we focus on improving the accuracy-variance trade-off for state-of-the-art differentially private machine learning (DP ML) methods. First, we design a general framewo…
Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture
Xinyu Tang, Saeed Mahloujifar, Liwei Song +4
Membership inference attacks are a key measure to evaluate privacy leakage in machine learning (ML) models. These attacks aim to distinguish training members from non-members by ex…
FRL: Federated Rank Learning
Hamid Mozaffari, Virat Shejwalkar, Amir Houmansadr
Federated learning (FL) allows mutually untrusted clients to collaboratively train a common machine learning model without sharing their private/proprietary training data among eac…
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning
Virat Shejwalkar, Amir Houmansadr, Peter Kairouz +1
While recent works have indicated that federated learning (FL) may be vulnerable to poisoning attacks by compromised clients, their real impact on production FL systems is not full…
GECKO: Reconciling Privacy, Accuracy and Efficiency in Embedded Deep Learning
Vasisht Duddu, Antoine Boutet, Virat Shejwalkar
Embedded systems demand on-device processing of data using Neural Networks (NNs) while conforming to the memory, power and computation constraints, leading to an efficiency and acc…
Quantifying Privacy Leakage in Graph Embedding
Vasisht Duddu, Antoine Boutet, Virat Shejwalkar
Graph embeddings have been proposed to map graph data to low dimensional space for downstream processing (e.g., node classification or link prediction). With the increasing collect…
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer
Hongyan Chang, Virat Shejwalkar, Reza Shokri +1
Collaborative (federated) learning enables multiple parties to train a model without sharing their private data, but through repeated sharing of the parameters of their local model…
Leveraging Prior Knowledge Asymmetries in the Design of Location Privacy-Preserving Mechanisms
Nazanin Takbiri, Virat Shejwalker, Amir Houmansadr +2
The prevalence of mobile devices and Location-Based Services (LBS) necessitate the study of Location Privacy-Preserving Mechanisms (LPPM). However, LPPMs reduce the utility of LBS…
Membership Privacy for Machine Learning Models Through Knowledge Transfer
Virat Shejwalkar, Amir Houmansadr
Large capacity machine learning (ML) models are prone to membership inference attacks (MIAs), which aim to infer whether the target sample is a member of the target model's trainin…