用证据学习与提示调优，实现隐私保护的皮肤病变分类。
Evidential Federated Learning for Skin Lesion Image Classification
- 用双提示结构增强预训练视觉变压器的分类能力。
- 通过注意力图知识蒸馏，实现不共享参数的跨客户端知识传递。
- 在真实分布式环境下验证，兼顾隐私与性能，适合医疗数据协作。
我们提出FedEvPrompt，一种融合证据深度学习、提示调优与知识蒸馏的联邦学习方法，用于分布式皮肤病变分类。该方法在冻结的预训练视觉变换器（ViT）模型前添加两类提示：b-prompts（低层基础视觉知识）和t-prompts（任务特定知识），并在证据学习框架下最大化类别证据。关键在于，联邦客户端间仅通过本地ViT生成的注意力图进行知识蒸馏，无需共享模型参数或合成图像，显著提升隐私保护性。算法采用轮次式训练机制，每轮包含本地模型训练与注意力图共享。在真实分布式环境下的ISIC2019数据集实验表明，FedEvPrompt优于基线联邦学习算法与知识蒸馏方法，且无需参数共享。结果证明，该方法有效应对数据异质性、不平衡及隐私保护等挑战。
原文摘要 · Abstract (English)
We introduce FedEvPrompt, a federated learning approach that integrates principles of evidential deep learning, prompt tuning, and knowledge distillation for distributed skin lesion classification. FedEvPrompt leverages two sets of prompts: b-prompts (for low-level basic visual knowledge) and t-prompts (for task-specific knowledge) prepended to frozen pre-trained Vision Transformer (ViT) models trained in an evidential learning framework to maximize class evidences. Crucially, knowledge sharing across federation clients is achieved only through knowledge distillation on attention maps generated by the local ViT models, ensuring enhanced privacy preservation compared to traditional parameter or synthetic image sharing methodologies. FedEvPrompt is optimized within a round-based learning paradigm, where each round involves training local models followed by attention maps sharing with all federation clients. Experimental validation conducted in a real distributed setting, on the ISIC2019 dataset, demonstrates the superior performance of FedEvPrompt against baseline federated learning algorithms and knowledge distillation methods, without sharing model parameters. In conclusion, FedEvPrompt offers a promising approach for federated learning, effectively addressing challenges such as data heterogeneity, imbalance, privacy preservation, and knowledge sharing.
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