2022
DOI: 10.48550/arxiv.2205.02438
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Uncertainty Minimization for Personalized Federated Semi-Supervised Learning

Abstract: 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… Show more

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