I am a fourth-year Ph.D. student in the School of Electrical and Computer Engineering at Purdue University, advised by Prof. Saurabh Bagchi in the Dependable Computing Systems Laboratory (DCSL). Previously, I received my M.S. in Computer Science and B.S. in Computer Engineering from Northwestern University in 2021.

My research interests lie in security and privacy of machine learning. During my time at Purdue, I have worked on projects focused on privacy attacks in distributed learning (federated learning) and also byzantine robustness. I am also currently engaged on projects relating to adversarial machine learning. In particular, I am exploring improving the fundamental tradeoff on adversarial robustness and generalization accuracy.

Here is a summary of my research.

Selected publications

For a full list of publications (along with YouTube videos and paper links), see the Publications page. My Google Scholar can also be found here.

LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation
Joshua C. Zhao, Atul Sharma, Ahmed Roushdy Elkord, Yahya H. Ezzeldin, Salman Avestimehr, Saurabh Bagchi
The 45th IEEE Symposium on Security and Privacy (S&P 2024)

Leak and Learn: An Attacker’s Cookbook to Train Using Leaked Data from Federated Learning
Joshua C. Zhao, Ahaan Dabholkar, Atul Sharma, Saurabh Bagchi
The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024)

The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning
Joshua C. Zhao, Ahmed Roushdy Elkordy, Atul Sharma, Yahya H. Ezzeldin, Salman Avestimehr, Saurabh Bagchi
The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023)

Awards and honors:

• Purdue Andrews fellowship
• Eta Kappa Nu, Beta Tau Chapter (Electrical Engineering Honor Society)
• Tau Beta Pi (Engineering Honor Society)