<ul><li>Reliable artificial intelligence (AI) models for medical image analysis often depend on large and diverse labeled datasets.</li><li>Federated learning (FL) struggles in highly non-independent and identically distributed (non-IID) settings, where institutions with more representative data may experience degraded performance.</li><li>Large-scale FL studies have been limited to adult datasets, neglecting the unique challenges posed by pediatric data, which introduces additional non-IID variability.</li><li>Using transfer learning from general-purpose self-supervised image representations, the study analyzed adult and pediatric chest radiographs and found that FL improved performance for smaller adult datasets but degraded performance for larger datasets and pediatric cases.</li></ul>

Boosting multi-demographic federated learning for chest x-ray analysis using general-purpose self-supervised representations

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