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 insights from different perspectives of labeled and unlabeled data.
In particular, Twin-sight concurrently trains a supervised model with a
supervised objective function while training an unsupervised model using an
unsupervised objective function. To enhance the synergy between these two
models, Twin-sight introduces a neighbourhood-preserving constraint, which
encourages the preservation of the neighbourhood relationship among data
features extracted by both models. Our comprehensive experiments on four
benchmark datasets provide substantial evidence that Twin-sight can
significantly outperform state-of-the-art methods across various experimental
settings, demonstrating the efficacy of the proposed Twin-sight.

我们提出了一种新颖的双模型范式 Twin-sight，通过不同角度从有标签和无标签数据中提供洞察力，以增强相互引导。该方法同时训练具有监督目标函数的监督模型和使用无监督目标函数训练的无监督模型，通过引入邻域保持约束增强两个模型之间的协同效应。通过在四个基准数据集上进行的全面实验，我们有实质性证据表明 Twin-sight 在各种实验设置下可以显著优于现有方法，验证了所提出 Twin-sight 的实效性。

具有极端标签不足的联邦模型的鲁棒训练

Robust Training of Federated Models with Extremely Label Deficiency

Automatic sentence summarization produces a shorter version of a sentence,
while preserving its most important information. A good summary is
characterized by language fluency and high information overlap with the source
sentence. We model these two aspects in an unsupervised objective function,
consisting of language modeling and semantic similarity metrics. We search for
a high-scoring summary by discrete optimization. Our proposed method achieves a
new state-of-the art for unsupervised sentence summarization according to ROUGE
scores. Additionally, we demonstrate that the commonly reported ROUGE F1 metric
is sensitive to summary length. Since this is unwillingly exploited in recent
work, we emphasize that future evaluation should explicitly group summarization
systems by output length brackets.

该研究提出了一种无监督的自动句子摘要方法，通过语言建模和语义相似度指标来优化摘要质量，从而在 ROUGE 分数方面达到了新的最高水平，同时强调未来的评估应该按输出长度分组。