Federated Magnetic Resonance Imaging (MRI) reconstruction enables multiple hospitals to collaborate distributedly without aggregating local data, thereby protecting patient privacy. However, the data heterogeneity caused by different MRI protocols, insufficient local training data, and limited communication bandwidth inevitably impair global model convergence and updating. In this paper, we propose a new algorithm, FedPR, to learn federated visual prompts in the null space of global prompt for MRI reconstruction. FedPR is a new federated paradigm that adopts a powerful pre-trained model while only learning and communicating the prompts with few learnable parameters, thereby significantly reducing communication costs and achieving competitive performance on limited local data. Moreover, to deal with catastrophic forgetting caused by data heterogeneity, FedPR also updates efficient federated visual prompts that project the local prompts into an approximate null space of the global prompt, thereby suppressing the interference of gradients on the server performance. Extensive experiments on federated MRI show that FedPR significantly outperforms state-of-the-art FL algorithms with <6% of communication costs when given the limited amount of local training data.
翻译:联合磁共振成像(MRI)重建使多个医院可以分布式合作而不需聚合本地数据,从而保护患者隐私。然而,由不同的MRI协议、不充足的本地训练数据和有限的通信带宽引起的数据异质性不可避免地会损害全局模型的收敛和更新。在本文中,我们提出了一种新的算法,称为FedPR,用于在MRI重建中学习联合零空间视觉提示。FedPR是一种新的联合范式,它采用强大的预训练模型,只学习和通信其中的少量可学习参数,从而显著降低通信成本,并在有限的本地数据上实现竞争性能。此外,为了解决由数据异质性引起的灾难性遗忘,FedPR还更新高效的联合视觉提示,将本地提示投影到全局提示的近似零空间中,从而抑制梯度对服务器性能的干扰。在联合MRI上的大量实验表明,在给定有限量的本地训练数据的情况下,FedPR在<6%的通信成本下显著优于现有联邦学习算法。