105 citations · 163 across the 23 of their papers we have counts for
8 papers · 1 filter
Benchmarking Algorithms for Federated Domain Generalization
Ruqi Bai, Saurabh Bagchi, David I. Inouye
While prior domain generalization (DG) benchmarks consider train-test dataset heterogeneity, we evaluate Federated DG which introduces federated learning (FL) specific challenges.…
The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning
Joshua C. Zhao, Ahmed Roushdy Elkordy, Atul Sharma +3
Secure aggregation promises a heightened level of privacy in federated learning, maintaining that a server only has access to a decrypted aggregate update. Within this setting, lin…
LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation
Joshua C. Zhao, Atul Sharma, Ahmed Roushdy Elkordy +3
Federated learning was introduced to enable machine learning over large decentralized datasets while promising privacy by eliminating the need for data sharing. Despite this, prior…
TESSERACT: Gradient Flip Score to Secure Federated Learning Against Model Poisoning Attacks
Atul Sharma, Wei Chen, Joshua Zhao +3
Federated learning---multi-party, distributed learning in a decentralized environment---is vulnerable to model poisoning attacks, even more so than centralized learning approaches.…
Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution Tests
Sean Kulinski, Saurabh Bagchi, David I. Inouye
While previous distribution shift detection approaches can identify if a shift has occurred, these approaches cannot localize which specific features have caused a distribution shi…
Anomaly Detection through Transfer Learning in Agriculture and Manufacturing IoT Systems
Mustafa Abdallah, Wo Jae Lee, Nithin Raghunathan +3
IoT systems have been facing increasingly sophisticated technical problems due to the growing complexity of these systems and their fast deployment practices. Consequently, IoT man…