Federated learning (FL) struggles with scalability in decentralized big data platforms due to data heterogeneity, communication bottlenecks, and computational inefficiencies. We propose Adaptive Feder
In recent years, Federated Learning (FL) has gained traction as a privacy-centric approach in medical imaging. This study explores the challenges posed by data heterogeneity on FL algorithms, using th
