54 citations · 54 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
MAUI: Reconstructing Private Client Data in Federated Transfer Learning
Ahaan Dabholkar, Atul Sharma, Z. Berkay Celik +1
Recent works in federated learning (FL) have shown the utility of leveraging transfer learning for balancing the benefits of FL and centralized learning. In this setting, federated…
Adversarial Attacks on Reinforcement Learning Agents for Command and Control
Ahaan Dabholkar, James Z. Hare, Mark Mittrick +4
Given the recent impact of Deep Reinforcement Learning in training agents to win complex games like StarCraft and DoTA(Defense Of The Ancients) - there has been a surge in research…
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…