Distributionally Robust Alignment for Medical Federated Vision-Language Pre-training Under Data Heterogeneity

• URL: http://arxiv.org/abs/2404.03854v3
• Date: Thu, 21 Nov 2024 21:08:40 GMT
• Title: Distributionally Robust Alignment for Medical Federated Vision-Language Pre-training Under Data Heterogeneity
• Authors: Zitao Shuai, Chenwei Wu, Zhengxu Tang, Liyue Shen,
• Abstract summary: We propose Federated Distributionally Robust Alignment (FedDRA) for medical vision-language pre-training. FedDRA achieves robust vision-language alignment under heterogeneous conditions. Our method also adapts well to various medical pre-training methods.
• Score: 4.84693589377679
• License:
• Abstract: Vision-language pre-training (VLP) has emerged as an effective scheme for multimodal representation learning, but its reliance on large-scale multimodal data poses significant challenges for medical applications. Federated learning (FL) offers a promising solution to scale up the dataset for medical VLP while preserving data privacy. However, we observe that client data heterogeneity in real-world scenarios could cause models to learn biased cross-modal alignment during local pre-training. This would limit the transferability of the federally learned representation model on downstream tasks. To address this challenge, we propose Federated Distributionally Robust Alignment (FedDRA), a framework for federated VLP that achieves robust vision-language alignment under heterogeneous conditions. Based on client datasets, we construct a distribution family that encompasses potential test-time domains, and apply a distributionally robust framework to optimize the pre-trained model's performance across this distribution space. This approach bridges the gap between pre-training samples and downstream applications. To avoid over-fitting on client-specific information, we use anchor representation from the global model to guide the local training, and adopt a two-stage approach to first tune deeper layers before updating the entire network. Extensive experiments on real-world datasets demonstrate FedDRA's effectiveness in enhancing medical federated VLP under data heterogeneity. Our method also adapts well to various medical pre-training methods.

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