OPFL: Optimistic Verification of Federated Learning via Empirical Boundary
OPFL calibrates an empirical boundary offline and uses it to distinguish benign numerical deviations from malicious manipulation, and adopts optimistic verification by post auditing only sampled training steps to reduce the cost of expensive MPC replay.
Hong-Xu Su, Jian-Zhu Yao, Xue-Chao Wang et al.
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