6 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…
Vision-Only Gaussian Splatting for Collaborative Semantic Occupancy Prediction
Cheng Chen, Hao Huang, Saurabh Bagchi
Collaborative perception enables connected vehicles to share information, overcoming occlusions and extending the limited sensing range inherent in single-agent (non-collaborative)…
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…
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…