提出新聚合策略，提升医疗影像联邦学习收敛速度
Enhancing the Convergence of Federated Learning Aggregation Strategies with Limited Data
- 设计新型联邦学习聚合方法，适应数据有限场景
- 在脑部MRI分类任务中显著加快收敛速度
- 适合医疗领域隐私敏感数据的协作建模
深度学习在医学图像诊断等领域应用广泛，但医疗数据常受隐私与法律限制，难以集中处理。为构建更鲁棒的模型，需跨研究机构协作，而联邦学习因其隐私保护特性成为关键方案。在此框架下，服务器聚合各参与方本地训练的模型以生成全局模型，该聚合过程至关重要。本文提出一种新型聚合策略，并应用于脑部磁共振图像分类任务。实验表明，所提方法在联邦学习迭代过程中显著提升了收敛性能，优于多种经典聚合策略，尤其在数据量有限条件下表现突出。
原文摘要 · Abstract (English)
The development of deep learning techniques is a leading field applied to cases in which medical data is used, particularly in cases of image diagnosis. This type of data has privacy and legal restrictions that in many cases prevent it from being processed from central servers. However, in this area collaboration between different research centers, in order to create models as robust as possible, trained with the largest quantity and diversity of data available, is a critical point to be taken into account. In this sense, the application of privacy aware distributed architectures, such as federated learning arises. When applying this type of architecture, the server aggregates the different local models trained with the data of each data owner to build a global model. This point is critical and therefore it is fundamental to analyze different ways of aggregation according to the use case, taking into account the distribution of the clients, the characteristics of the model, etc. In this paper we propose a novel aggregation strategy and we apply it to a use case of cerebral magnetic resonance image classification. In this use case the aggregation function proposed manages to improve the convergence obtained over the rounds of the federated learning process in relation to different aggregation strategies classically implemented and applied.
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