Federated Learning

Definition ∞ Federated learning is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging their data. Instead of aggregating data into a central location, the model is trained locally, and only the model updates are shared and aggregated. This approach enhances data privacy and reduces communication overhead.
Context ∞ Federated learning is gaining attention in discussions about privacy-preserving technologies within the digital asset space, particularly for applications involving user data or AI-driven analytics on decentralized platforms. News may cover its application in improving decentralized applications (dApps) or enhancing the security and personalization of user experiences without compromising sensitive information. Its potential to enable collaborative model training while respecting data sovereignty is a key aspect of its relevance.