92 citations · 224 across the 14 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…