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Federated Learning Privacy

Definition

Federated learning privacy is a method of training machine learning models across multiple decentralized devices or servers holding local data samples without exchanging the data itself. This approach protects sensitive user information by only sharing model updates, such as weight adjustments, rather than raw data, with a central server or across peer-to-peer networks. It minimizes the risk of data leakage and preserves individual data autonomy, making it particularly valuable for applications involving highly confidential information like medical records or financial transactions. Federated learning privacy enables collaborative AI development while adhering to strict data protection regulations and user consent principles. It safeguards personal data effectively.