FedSCA：基于相似性引导的异构医学图像分割联合调谐
摘要
Transformer 基底模型(FMs)最近在医学图像分割方面展现出卓越的性能。然而，由于受限于孤立医院内医学图像数据集的规模，且由于隐私原因受限于数据集中化，这些模型的规模化具有挑战。这些限制，以及基底模型数据密集型的特性，阻碍了其更广泛的应用。将联合学习(FL)与基底模型(FLFM)微调相结合，提供了一个可能的解决方案，通过在不共享数据的情况下实现协作模型训练，从而使FMs能够利用来自各医院/客户的敏感医学图像数据的多样化池。然而，来自各客户的数据非独立同分布(non-IID)，以及联合环境中的计算和通信限制， presents an additional challenge that limits further performance improvements and remains inadequately addressed in existing studies. In this work, we propose a novel FLFM fine-tuning framework, \underline{\textbf{Fed}}erated tuning with \underline{\textbf{S}}imilarity-guided \underline{\textbf{C}}ollaborative \underline{\textbf{A}}ggregation (FedSCA), encompassing all phases of the FL process. This includes (1) specially designed parameter-efficient fine-tuning (PEFT) for local client training to enhance computational efficiency; (2) partial low-level adapter transmission for communication efficiency; and (3) similarity-guided collaborative aggregation (SGCA) on the server side to address non-IID issues. Extensive experiments on three FL benchmarks for medical image segmentation demonstrate the effectiveness of our proposed FedSCA, establishing new SOTA performance.
引用
@article{arxiv.2503.15390,
title = {FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation},
author = {Yumin Zhang and Yan Gao and Haoran Duan and Hanqing Guo and Tejal Shah and Rajiv Ranjan and Bo Wei},
journal= {arXiv preprint arXiv:2503.15390},
year = {2025}
}