On the Fundamental Limits of Formally (Dis)Proving Robustness in Proof-of-Learning. - Congyu Fang; Hengrui Jia 001; Anvith Thudi; Mohammad Yaghini; Christopher A. Choquette-Choo; Natalie Dullerud; Varun Chandrasekaran; Nicolas Papernot (CoRR, 2022)

Proof-of-Learning is Currently More Broken Than You Think. - Congyu Fang; Hengrui Jia 001; Anvith Thudi; Mohammad Yaghini; Christopher A. Choquette-Choo; Natalie Dullerud; Varun Chandrasekaran; Nicolas Papernot (EuroS&P, 2023)

Robust and Actively Secure Serverless Collaborative Learning. - Nicholas Franzese; Adam Dziedzic; Christopher A. Choquette-Choo; Mark R. Thomas; Muhammad Ahmad Kaleem; Stephan Rabanser; Congyu Fang; Somesh Jha; Nicolas Papernot; Xiao Wang 012 (NeurIPS, 2023)

Robust and Actively Secure Serverless Collaborative Learning. - Olive Franzese; Adam Dziedzic; Christopher A. Choquette-Choo; Mark R. Thomas; Muhammad Ahmad Kaleem; Stephan Rabanser; Congyu Fang; Somesh Jha; Nicolas Papernot; Xiao Wang 012 (CoRR, 2023)

Privacy-Preserving Federated Learning for Coverage Prediction. - Congyu Fang; Akram Bin Sediq; Hamza Umit Sokun; Israfil Bahceci; A Ahmed Ibrahim; Nicolas Papernot (PIMRC, 2024)

Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data. - Congyu Fang; Adam Dziedzic; Lin Zhang; Laura Oliva; Amol Verma; Fahad Razak; Nicolas Papernot; Bo Wang (CoRR, 2024)

Secure Noise Sampling for Differentially Private Collaborative Learning. - Olive Franzese; Congyu Fang; Radhika Garg 002; Somesh Jha; Nicolas Papernot; Xiao Wang 012; Adam Dziedzic (IACR Cryptol. ePrint Arch., 2025)

Exploring the Design Space of 3D MLLMs for CT Report Generation. - Mohammed Baharoon; Jun Ma; Congyu Fang; Augustin Toma; Bo Wang (CoRR, 2025)