Federated learning (FL) effectively mitigates the data silo challenge brought about by policies and privacy concerns, implicitly harnessing more data for deep model training. However, traditional centralized FL models grapple with diverse multi-center data, especially in the face of significant data heterogeneity, notably in medical contexts. In the realm of medical image segmentation, the growing imperative to curtail annotation costs has amplified the importance of weakly-supervised techniques which utilize sparse annotations such as points, scribbles, etc. A pragmatic FL paradigm shall accommodate diverse annotation formats across different sites, which research topic remains under-investigated. In such context, we propose a novel personalized FL framework with learnable prompt and aggregation (FedLPPA) to uniformly leverage heterogeneous weak supervision for medical image segmentation. In FedLPPA, a learnable universal knowledge prompt is maintained, complemented by multiple learnable personalized data distribution prompts and prompts representing the supervision sparsity. Integrated with sample features through a dual-attention mechanism, those prompts empower each local task decoder to adeptly adjust to both the local distribution and the supervision form. Concurrently, a dual-decoder strategy, predicated on prompt similarity, is introduced for enhancing the generation of pseudo-labels in weakly-supervised learning, alleviating overfitting and noise accumulation inherent to local data, while an adaptable aggregation method is employed to customize the task decoder on a parameter-wise basis. Extensive experiments on four distinct medical image segmentation tasks involving different modalities underscore the superiority of FedLPPA, with its efficacy closely parallels that of fully supervised centralized training. Our code and data will be available.

翻译：联邦学习（FL）能有效缓解由政策和隐私问题带来的数据孤岛挑战，隐式地利用更多数据进行深度模型训练。然而，传统的集中式联邦学习模型在处理多样化的多中心数据时面临困难，尤其是在面临显著数据异质性的场景下，这在医学领域尤为突出。在医学图像分割领域，降低标注成本的迫切需求日益增长，这提升了利用点、涂鸦等稀疏标注的弱监督技术的重要性。一个实用的联邦学习范式应能适应不同站点间多样的标注格式，而这一研究方向目前仍未得到充分探索。在此背景下，我们提出了一种新颖的个性化联邦学习框架——具备可学习提示与聚合的FedLPPA，以统一利用异构弱监督进行医学图像分割。在FedLPPA中，维护了一个可学习的通用知识提示，并辅以多个可学习的个性化数据分布提示以及代表监督稀疏性的提示。这些提示通过双重注意力机制与样本特征集成，使每个本地任务解码器能够灵活适应本地数据分布和监督形式。同时，引入了一种基于提示相似性的双重解码器策略，以增强弱监督学习中伪标签的生成，缓解本地数据固有的过拟合和噪声累积问题；并采用了一种自适应聚合方法，在参数层面为任务解码器进行定制。在涉及不同模态的四个不同医学图像分割任务上进行的大量实验，证明了FedLPPA的优越性，其效能与完全监督的集中式训练结果非常接近。我们的代码和数据将公开提供。