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cs.CR2026
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…
cs.CR2026
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…
cs.CR2025
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…