Closing the Alignment-Maturity Gap in Federated Prototype Learning 事件

Closing the Alignment-Maturity Gap in Federated Prototype Learning arXiv:2606.02172v1 Announce Type: cross Abstract: Learning discriminative visual representations from distributed, heterogeneous data is a fundamental challenge in Federated Learning (FL). Prototype-based methods address statistical heterogeneity by sharing class-level representations across clients but create a distance-dependent gradient pressure that is particularly severe during early training rounds: alignment pressure appl