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 three 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：联邦学习有效缓解了由政策和隐私顾虑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，传统集中式联邦学习模型难以处理多中心异构数据，尤其在面对显著的数据异质性时，医疗场景中的这一问题更为突出。在医学图像分割领域，降低标注成本的迫切需求凸显了弱监督技术的重要性，该类技术利用点、涂鸦等稀疏标注实现分割。实用的联邦学习范式应能适应不同站点的多样化标注格式，而这一研究课题尚待深入探索。为此，我们提出了一种新颖的个性化联邦学习框架——基于可学习提示与聚合的FedLPPA，以统一利用异质弱监督信息进行医学图像分割。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及表征监督稀疏性的提示。通过双重注意力机制与样本特征融合后，这些提示使每个局部任务解码器能够灵活适应本地数据分布与监督形式。同时，基于提示相似性的双解码器策略被引入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与噪声累积问题，并采用一种可适应性聚合方法在参数级别上定制任务解码器。在涉及不同模态的三项医学图像分割任务上的大量实验表明，FedLPPA具有优越性能，其效果与全监督集中式训练高度接近。我们的代码与数据将公开提供。