AI RESEARCH
Closing the Alignment-Maturity Gap in Federated Prototype Learning
arXiv CS.LG
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ArXi:2606.02172v1 Announce Type: new 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