Federated Intelligence refers to a distributed artificial intelligence framework where multiple participants collaboratively train a shared model without centralizing their raw data. Instead of pooling data, individual nodes compute local model updates, which are then aggregated to improve the global model. This approach enhances data privacy and security, as sensitive information remains on local devices. It is particularly relevant in blockchain environments where data sovereignty is a primary concern.
Context
The application of federated intelligence in decentralized networks is gaining traction for privacy-preserving machine learning solutions. News frequently covers projects aiming to use federated intelligence to improve fraud detection or market prediction in the digital asset space. Discussions often focus on the challenges of ensuring model integrity and preventing data poisoning attacks within federated intelligence systems.
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