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：联邦磁共振成像重建使多家医院能够在无需聚合本地数据的情况下分布式协作，从而保护患者隐私。然而，由不同MRI协议、本地训练数据不足及有限通信带宽所导致的数据异质性，不可避免地损害了全局模型的收敛与更新。本文提出一种新算法FedPR，用于在全局提示的零空间中学习面向MRI重建的联邦视觉提示。FedPR是一种新型联邦范式，采用强大的预训练模型，仅需学习并通信参数极少的视觉提示，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性能。此外，为应对数据异质性引发的灾难性遗忘，FedPR还更新高效的联邦视觉提示，将本地提示投影到全局提示的近似零空间，从而抑制梯度对服务器性能的干扰。在联邦MRI上的大量实验表明，当本地训练数据有限时，FedPR以不到6%的通信成本，显著优于最先进的联邦学习算法。