The Crowd Training Protocol introduces an innovative ownership model for machine learning, rewarding contributors based on their roles as ecosystem supporters, data providers, or computational resource contributors. By leveraging inference slashing, seed rounds, and ownership inflation, the protocol ensures equitable compensation, fosters collaboration, and sustains the development and maintenance of decentralized machine learning models.
Published: January 21, 2025
Read MoreThe article explores a hybrid framework for addressing the machine learning trilemma—privacy preservation, computational efficiency, and model performance—by combining Fully Homomorphic Encryption (FHE) and Distributed Trusted Execution Environments (DTEEs).
Published: January 19, 2025
Read MoreThe article examines the concept of 'Reverse Robin Hood Attacks,' where resources are extracted from less fortunate participants and funneled to wealthier entities, and its implications for the Crowd Training Protocol.
Published: January 12, 2025
Read MoreThe Crowd Training Protocol leverages blockchain technology and decentralized governance to enable collaborative training and management of machine learning models while ensuring transparency, fairness, and accountability.
Published: January 12, 2025
Read MoreThis article explores how blockchain-enabled crowd training integrates decentralized AI development with collaborative governance, leveraging innovations like federated learning, compute-intensive smart contracts, and the Private Weights, Public Architecture (PWPA) paradigm.
Published: January 10, 2025
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