<ul><li>Federated Learning (FL) in healthcare faces challenges like data heterogeneity and unreliable contributions.</li><li>A new study introduces a peer-driven reputation mechanism for federated healthcare to improve model aggregation.</li><li>The proposed approach integrates decentralized peer feedback and clustering-based noise handling, ensuring privacy of sensitive information.</li><li>Experimental evaluations show that the method successfully addresses data heterogeneity and reputation deficit in FL, outperforming systems without a reputation mechanism.</li></ul>

Enhancing Federated Survival Analysis through Peer-Driven Client Reputation in Healthcare

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