Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators

arXiv:2307.05358v3 Announce Type: replace
Abstract: Federated learning has become a popular method to learn from decentralized heterogeneous data. Federated semi-supervised learning (FSSL) emerges to train models from a small fraction of labeled data due to label scarcity on decentralized clients. Existing FSSL methods assume independent and identically distributed (IID) labeled data across clients and consistent class distribution between labeled and unlabeled data within a client. This work studies a more practical and challenging scenario of FSSL, where data distribution is …

abstract arxiv become cs.ai cs.lg data decentralized distributed federated learning independent learn popular regulators semi-supervised semi-supervised learning small supervised learning train type