Gradient sharing is a technique used in distributed machine learning, particularly in federated learning, where multiple parties collaboratively train a model without directly sharing their raw data. Instead, participants compute local gradients of the model based on their private data and then share these gradients with a central server or among themselves. This method allows for collective model improvement while preserving data privacy. It is a key component in privacy-preserving AI.
Context
While not a direct crypto term, gradient sharing holds significant implications for privacy-preserving machine learning applications on decentralized networks. News in the Web3 AI space might discuss how blockchain could facilitate secure and verifiable gradient sharing among participants. This approach could enable decentralized AI training platforms where data privacy is paramount, attracting attention from projects focused on secure data collaboration.
ZKPoT, a novel zk-SNARK-based consensus, validates model performance privately, fundamentally enabling scalable and secure decentralized AI collaboration.
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