Federated Data Pruning via Activation Stability

Federated Learning (FL) keeps data on-device, but pays for it in compute: clients spend most of every round re-processing data that have already stopped contributing to learning. Existing FL data-pruning methods rely on static importance scores, server-side validation, or aggregated centroids: assumptions that break under privacy constraints and non-IID heterogeneity. In this work, we ask whether a purely local, training-aware data redundancy signal exists, and answer in the affirmative. We introduce activation stability-based data pruning at the batch level for FL, a lightweight, architecture-agnostic plug-in that permanently removes batches whose mean hidden-activation variance saturates across iterations, a signal already produced by the local forward pass, requiring no extra communication, no server data, and no architectural change. We show that activation stability and client drift evolve on decoupled time scales, explaining why local pruning remains reliable under heterogeneity. Across CIFAR-10/100, SVHN, and ImageNet-1K with ResNet-18/50 and CvT, our framework discards up to 45% of training batches at almost no accuracy cost, saving up to 45% of the GPU node-hours.