Definition ∞ Secure Model Aggregation describes a technique, often employed in decentralized machine learning or privacy-preserving data analysis, where multiple participants contribute local computational models or data subsets to create a combined, more robust global model. This process is executed in a manner that protects the privacy of individual contributions, preventing the exposure of sensitive underlying data. It frequently utilizes advanced cryptographic methods.
Context ∞ Secure model aggregation is gaining relevance in blockchain and artificial intelligence discussions, particularly concerning data privacy and collaborative computation in decentralized networks. News might cover new research, protocol implementations, or partnerships focused on applying these techniques to areas like fraud detection or risk assessment using distributed data. The discussion centers on achieving collective intelligence from disparate data sources while maintaining stringent privacy and security standards.
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