SplitFed Learning, a combination of Federated and Split Learning (FL and SL), is one of the most recent developments in the decentralized machine learning domain. In SplitFed learning, a model is trained by clients and a server collaboratively. For image segmentation, labels are created at each client independently and, therefore, are subject to clients' bias, inaccuracies, and inconsistencies. In this paper, we propose a data quality-based adaptive averaging strategy for SplitFed learning, called QA-SplitFed, to cope with the variation of annotated ground truth (GT) quality over multiple clients. The proposed method is compared against five state-of-the-art model averaging methods on the task of learning human embryo image segmentation. Our experiments show that all five baseline methods fail to maintain accuracy as the number of corrupted clients increases. QA-SplitFed, however, copes effectively with corruption as long as there is at least one uncorrupted client.

翻译：分割联邦学习（SplitFed Learning）是联邦学习（FL）与分割学习（SL）的结合，是去中心化机器学习领域的最新进展之一。在分割联邦学习中，模型由客户端和服务器协作训练。针对图像分割任务，标签由各客户端独立生成，因此易受客户端偏差、不精确性和不一致性的影响。本文提出一种基于数据质量的自适应平均策略——QA-SplitFed（Quality-Adaptive Split-Fed），以应对多个客户端间标注真实值（GT）质量的变化。我们将该方法与五种最先进的模型平均方法在人类胚胎图像分割学习任务上进行比较。实验表明，随着受损客户端数量的增加，所有五种基线方法均无法维持精度。然而，只要存在至少一个未受损客户端，QA-SplitFed就能有效应对数据损坏问题。