Gradient sharing risk pertains to the potential for information leakage or exploitation when gradients, which represent the direction and magnitude of change, are exchanged in distributed machine learning models. In cryptocurrency contexts, this can relate to privacy concerns in federated learning applications on blockchains. Malicious actors might reconstruct sensitive training data from shared gradients. This risk threatens the confidentiality of individual data contributions.
Context
Gradient sharing risk is a growing area of concern at the intersection of artificial intelligence and blockchain technology, particularly in privacy-preserving computations. Discussions often involve methods to secure gradient exchange, such as differential privacy and secure aggregation techniques. News articles address this risk when reporting on advancements in decentralized AI and data privacy solutions.
Zero-Knowledge Proof of Training (ZKPoT) is a new consensus primitive that cryptographically verifies model accuracy without exposing private training data, resolving the privacy-utility conflict in decentralized AI.
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