Federated Learning: AI Without Centralized Data Meta Description Explore federated learning, a revolutionary approach in artificial intelligence that enables model training across decentralized devices, enhancing privacy and efficiency. Introduction In the evolving landscape of artificial intelligence (AI), federated learning has emerged as a transformative approach that allows AI models to be trained across multiple decentralized devices or servers holding local data samples, without exchanging them. This method addresses critical concerns related to data privacy, security, and efficiency, making it particularly valuable in sectors like healthcare, finance, and mobile applications. Understanding Federated Learning Federated learning is a machine learning technique where the training process is distributed across multiple devices or servers. Each participant trains a shared model on its local data and only shares model updates (such as gradients) with a central server. The centr...
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