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Uncertainty Minimization for Personalized Federated Semi-Supervised Learning. (arXiv:2205.02438v2 [cs.LG] UPDATED)
cs.LG updates on arXiv.org arxiv.org
Since federated learning (FL) has been introduced as a decentralized learning
technique with privacy preservation, statistical heterogeneity of distributed
data stays the main obstacle to achieve robust performance and stable
convergence in FL applications. Model personalization methods have been studied
to overcome this problem. However, existing approaches are mainly under the
prerequisite of fully labeled data, which is unrealistic in practice due to the
requirement of expertise. The primary issue caused by partial-labeled condition
is that, clients with deficient labeled …
arxiv learning lg personalized semi-supervised semi-supervised learning supervised learning uncertainty