Decentralized federated learning is a machine learning approach where multiple participants collaboratively train a shared model without centralizing their raw data. Instead, local models are trained on individual datasets, and only model updates are exchanged and aggregated across a decentralized network. This method prioritizes data privacy and security by keeping sensitive information localized. It enables collective intelligence while preserving data sovereignty.
Context
Decentralized federated learning is an emerging area with potential applications in Web3 and data privacy, particularly for digital assets that rely on collective data insights. Current discussions explore its use in decentralized AI marketplaces and privacy-preserving data analytics on blockchain. Challenges include ensuring the integrity of model updates and incentivizing honest participation within a distributed setting.
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