Did you know that federated learning has been outperforming traditional centralized learning models in medical diagnosis for cancer patients, even when the local data was highly imbalanced and biased? This was observed in a 2022 study published in the journal Nature Medicine, where researchers developed a federated learning framework to diagnose breast cancer from medical images.
In this study, multiple hospitals contributed their local datasets, each containing a small number of patients. The datasets were imbalanced, with some patients having a high number of malignant tumors and others having a low number. Centralized learning models would typically struggle with such imbalanced data, but the federated learning approach allowed the model to learn from each local dataset without compromising patient data confidentiality.
The results showed that the federated learning model achieved a high accuracy of 95.6% in diagnosing breast cancer, outperforming traditional centralized models...
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