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Decentralized Machine Learning

Background

Decentralized machine learning (DML) is a type of machine learning where data is distributed among many different nodes in a network, rather than being stored in a centralized location. This allows for improved scalability and flexibility, as well as increased security and privacy. DML is often used in conjunction with blockchain technology, as the two can complement each other well. For example, DML can be used to train models that are then deployed on a blockchain, ensuring that the models are tamper-proof and immutable. There are a number of different protocols and platforms that enable DML, such as Ethereum, IPFS, and BigchainDB.

Some of the emerging trends in decentralized machine learning include the use of federated learning, the use of blockchain technology, and the use of distributed ledger technologies.

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