54 citations · 58 across the 9 of their papers we have counts for
9 papers
Color Matters: Trigger Color Affects Success in Federated Backdoor Attacks
Kavindu Herath, Joshua C. Zhao, Saurabh Bagchi
Federated learning is vulnerable to backdoor attacks in which malicious clients inject poisoned updates while preserving benign-task performance. In this paper, we study a semantic…
Beyond Corner Patches: Semantics-Aware Backdoor Attack in Federated Learning
Kavindu Herath, Joshua Zhao, Saurabh Bagchi
Backdoor attacks on federated learning (FL) are most often evaluated with synthetic corner patches or out-of-distribution (OOD) patterns that are unlikely to arise in practice. In…
Are Fast Methods Stable in Adversarially Robust Transfer Learning?
Joshua C. Zhao, Saurabh Bagchi
Transfer learning is often used to decrease the computational cost of model training, as fine-tuning a model allows a downstream task to leverage the features learned from the pre-…
The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape
Joshua C. Zhao, Saurabh Bagchi, Salman Avestimehr +7
Deep learning has shown incredible potential across a wide array of tasks, and accompanied by this growth has been an insatiable appetite for data. However, a large amount of data…
Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning
Joshua C. Zhao, Ahaan Dabholkar, Atul Sharma +1
Federated learning is a decentralized learning paradigm introduced to preserve privacy of client data. Despite this, prior work has shown that an attacker at the server can still r…
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.…
An End-to-End Solution for Effectively Demoting Watermarked Images in Image Search
Ning Ma, Xin Zhao, Mark Bolin
We propose an end-to-end solution, from watermark feature generation to metric design, for effectively demoting watermarked images surfed by a real world image search engine. We us…