Blockchain-secured learning refers to the application of blockchain technology to enhance the security, transparency, and integrity of machine learning processes. This involves recording training data provenance, model updates, and prediction results on an immutable ledger. The objective is to ensure data privacy, verifiable model behavior, and auditable AI systems. It mitigates risks related to data tampering and algorithmic bias.
Context
The intersection of blockchain and machine learning is a rapidly advancing field, addressing critical concerns regarding trust and accountability in AI. Discussions center on balancing computational overhead with the security benefits of decentralization for large-scale learning models. A critical future development involves scaling solutions that permit efficient, privacy-preserving machine learning computations on distributed ledgers, fostering verifiable AI systems.
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