Federated Learning is a decentralized machine learning approach that enables AI models to train across multiple devices or servers without sharing raw data.
Unlike traditional machine learning, where data is collected and stored in a central location for model training, federated learning allows individual devices, such as smartphones, Internet of Things devices, or edge servers, to process data locally and send only model updates to a central server.
This ensures that sensitive information remains on the user's device, significantly enhancing privacy while still enabling AI systems to improve over time.
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