Blockchain-Secured FL refers to federated learning models where a blockchain verifies and records updates to the shared model. This method enhances data privacy by keeping individual datasets localized while leveraging decentralized ledger technology for secure aggregation. The blockchain provides an immutable audit trail for model contributions and parameter updates. It ensures transparency and integrity in collaborative machine learning processes.
Context
The integration of blockchain with federated learning addresses critical concerns about data provenance and model integrity in artificial intelligence applications. Current discussions center on optimizing the trade-off between blockchain overhead and the security benefits it provides. This area is under active research for applications requiring high data privacy and verifiable computations.
Research introduces ZKPoT consensus, leveraging zk-SNARKs to cryptographically verify machine learning contributions without exposing private training data or model parameters.
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