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Enhancing Federated Learning Security with a Defense Framework Against Adversarial Attacks in Privacy-Sensitive Healthcare Applications
Federated learning (FL) is a cutting-edge method of collaborative machine learning that lets organizations or companies train models without exchanging personal information. Adversarial attacks such as data poisoning, model poisoning, backdoor attacks, and man-in-the-middle attacks could compromise its accuracy and reliability. Ensuring resistance against such risks is crucial as FL… Read full abstract & cite →
Federated learning
machine learning
privacy
adversarial attacks
defense framework
global model
healthcare
disease prediction
