Robust Training of Federated Models with Extremely Label Deficiency

arXiv:2402.14430v1 Announce Type: new
Abstract: Federated semi-supervised learning (FSSL) has emerged as a powerful paradigm for collaboratively training machine learning models using distributed data with label deficiency. Advanced FSSL methods predominantly focus on training a single model on each client. However, this approach could lead to a discrepancy between the objective functions of labeled and unlabeled data, resulting in gradient conflicts. To alleviate gradient conflict, we propose a novel twin-model paradigm, called Twin-sight, designed to enhance mutual guidance by providing …

abstract advanced arxiv client cs.lg data distributed distributed data focus machine machine learning machine learning models paradigm robust semi-supervised semi-supervised learning supervised learning training type