Federated learning (FL) struggles with scalability in decentralized big data platforms due to data heterogeneity, communication bottlenecks, and computational inefficiencies. We propose Adaptive Feder
Top-1 to top-5 accuracy of our naive and of our informed CASIMAC on the fashion-mnist data set. In the naive approach we use a purely Euclidean distance metric between the images, whereas the informed
Federated learning (FL) struggles with scalability in decentralized big data platforms due to data heterogeneity, communication bottlenecks, and computational inefficiencies. We propose Adaptive Feder
