How do federated learning approaches differ from traditional machine learning in terms of data handling?
- Federated learning doesn't use data
- Federated learning relies on centralized data storage
- Federated learning trains models on decentralized data
- Traditional machine learning trains models on a single dataset
Federated learning trains machine learning models on decentralized data sources without transferring them to a central server. This approach is privacy-preserving and efficient. In contrast, traditional machine learning typically trains models on a single, centralized dataset, which may raise data privacy concerns.
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