<ul><li>This paper presents a data privacy protection framework based on federated learning.</li><li>Federated learning reduces the risk of privacy breaches by training the model locally on each client and sharing only model parameters.</li><li>The experiment demonstrates the efficiency and privacy protection ability of federated learning for medical, financial, and user data.</li><li>Federated learning enables effective cross-domain data collaboration while ensuring data privacy.</li></ul>

Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration

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