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