4 papers
OPFL: Optimistic Verification of Federated Learning via Empirical Boundary
Hongxu Su, Jianzhu Yao, Xuechao Wang +1
Federated learning enables multiple clients to collaboratively train models without sharing their private data. However, the lack of visibility into local training makes it difficu…
OVIG: Optimistic Verification of AI Training Integrity via Gradient Signals
Hongxu Su, Jianzhu Yao, Huan Zhang +2
The rapid growth of AI has increased the demand for domain-specific models. Post-training of open-source models offers a more economical way to meet this growing demand, but the co…
GasLiteAA: Optimizing ERC-4337 for Efficient and Secure Gas Sponsorship
Hongxu Su, Mingzhe Liu, Jie Xu +2
ERC-4337, the Ethereum account abstraction standard, simplifies account management and transaction fee payment in decentralized applications by introducing programmable smart contr…
TAO: Tolerance-Aware Optimistic Verification for Floating-Point Neural Networks
Jianzhu Yao, Hongxu Su, Taobo Liao +4
Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces). Yet ML-as-a-Service reveals little about what actually ran or whether…