Cryptographic techniques enabling verification that a machine learning model executed correctly on specific inputs without revealing model weights, training data, or private inputs. ZKML bridges zero-knowledge proofs and machine learning, allowing on-chain verification of off-chain AI inference with privacy guarantees.
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