Decentralized Federated Learning on the Oasis Network by Dawn Song (Devcon 5)
Machine learning is being adopted more and more broadly in technology. Such success is largely due to a combination of algorithmic breakthroughs, computation resource improvements, and the access to a large amount of diverse training data. The collection of data can raise concerns about siloing, security, and user privacy.
In this talk, I will highlight a new blockchain-based machine learning technology that allows users to share their data, train models in a fully decentralized way, and incentivize end users to keep their data on the network using the Oasis network. This technology, called HiveMind, leverages a federated learning framework to reduce overhead both in communication and computation. In addition, the talk will highlight the benefits of a novel blockchain-based secure aggregation protocol that ensures client-level differential privacy, and thus prevents information leakage from trained model parameters.
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