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SplitFed Learning, a combination of Federated and Split Learning (FL and SL),is one of the most recent developments in the decentralized machine learningdomain. In SplitFed learning, a model is trained by clients and a servercollaboratively. For image segmentation, labels are created at each clientindependently and, therefore, are subject to clients' bias, inaccuracies, andinconsistencies. In this paper, we propose a data quality-based adaptiveaveraging strategy for SplitFed learning, called QA-SplitFed, to cope with thevariation of annotated ground truth (GT) quality over multiple clients. Theproposed method is compared against five state-of-the-art model averagingmethods on the task of learning human embryo image segmentation. Ourexperiments show that all five baseline methods fail to maintain accuracy asthe number of corrupted clients increases. QA-SplitFed, however, copeseffectively with corruption as long as there is at least one uncorruptedclient.

Quality-Adaptive Split-Federated Learning for Segmenting Medical Images with Inaccurate Annotations