Vision-language pre-training (VLP) has arised as an efficient scheme for
multimodal representation learning, but it requires large-scale multimodal data
for pre-training, making it an obstacle especially for biomedical applications.
To overcome the data limitation, federated learning (FL) can be a promising
strategy to scale up the dataset for biomedical VLP while protecting data
privacy. However, client data are often heterogeneous in real-world scenarios,
and we observe that local training on heterogeneous client data would distort
the multimodal representation learning and lead to biased cross-modal
alignment. To address this challenge, we propose Federated distributional
Robust Guidance-Based (FedRGB) learning framework for federated VLP with
robustness to data heterogeneity. Specifically, we utilize a guidance-based
local training scheme to reduce feature distortions, and employ a
distribution-based min-max optimization to learn unbiased cross-modal
alignment. The experiments on real-world datasets show our method successfully
promotes efficient federated multimodal learning for biomedical VLP with data
heterogeneity.

使用具有数据异质性鲁棒性的联邦分布式引导学习框架（FedRGB）解决生物医学 VLP 中异质数据的挑战，从而促进了高效的联邦多模态学习。

缓解联邦多模式学习中的异质性：基于生物医学视觉语言预训练

Mitigating Heterogeneity in Federated Multimodal Learning with  Biomedical Vision-Language Pre-training

Self-supervised learning in vision-language processing exploits semantic
alignment between imaging and text modalities. Prior work in biomedical VLP has
mostly relied on the alignment of single image and report pairs even though
clinical notes commonly refer to prior images. This does not only introduce
poor alignment between the modalities but also a missed opportunity to exploit
rich self-supervision through existing temporal content in the data. In this
work, we explicitly account for prior images and reports when available during
both training and fine-tuning. Our approach, named BioViL-T, uses a
CNN-Transformer hybrid multi-image encoder trained jointly with a text model.
It is designed to be versatile to arising challenges such as pose variations
and missing input images across time. The resulting model excels on downstream
tasks both in single- and multi-image setups, achieving state-of-the-art
performance on (I) progression classification, (II) phrase grounding, and (III)
report generation, whilst offering consistent improvements on disease
classification and sentence-similarity tasks. We release a novel multi-modal
temporal benchmark dataset, MS-CXR-T, to quantify the quality of
vision-language representations in terms of temporal semantics. Our
experimental results show the advantages of incorporating prior images and
reports to make most use of the data.

本文提出一种名为 BioViL-T 的方法，使用了了同时训练和微调过程中的先前图像和报告，通过 CNN-Transformer 混合多图像编码器与文本模型协同训练，获得了最先进的性能，在单图像和多图像设置下实现了进展分类，短语接地和报告生成，并在疾病分类和句子相似性任务上持续提供改进，并使用一个新的多模态时间基准数据集 MS-CXR-T 表征了视觉语言表示的优劣。