Decentralized machine learning involves distributing the training and execution of machine learning models across multiple independent nodes. This approach typically utilizes blockchain technology or distributed ledger systems to coordinate participants and record model updates. It promotes data privacy, reduces reliance on central servers, and can enable collaborative model development without a single point of control. Participants contribute computational resources and data, often earning rewards for their efforts.
Context
The application of decentralized machine learning is gaining attention for its potential to address privacy concerns and data monopolies in AI. Current discussions often focus on developing efficient consensus mechanisms for model aggregation and ensuring data integrity across disparate sources. Its relevance in crypto news relates to projects building AI capabilities on blockchain networks, offering new paradigms for data ownership and AI governance.
This research introduces zkFL, a novel framework leveraging zero-knowledge proofs and blockchain to secure federated learning against malicious aggregators, fostering trust in collaborative AI systems.
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