Decentralized Machine Learning (DeML)
Decentralized Machine Learning involves training AI models across multiple, distributed nodes or devices without a central server. In crypto, this can mean using on-chain data from various nodes, or off-chain data computed by decentralized entities, to build a collective model. Incentives for participation, often token-based, encourage data sharing and secure computation from a wider range of sources.
Imagine a global collaborative research project where thousands of scientists contribute their unique data and computing power to a single project, without ever needing a central lab, sharing the credit and rewards based on their contributions.
DeML enhances privacy and robustness for crypto applications by removing single points of failure and central data custodians, which is crucial for building trustless AI services on blockchains.
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