  The widespread application of artificial intelligence in health research is
currently hampered by limitations in data availability. Distributed learning
methods such as federated learning (FL) and shared learning (SL) are introduced
to solve this problem as well as data management and ownership issues with
their different strengths and weaknesses. The recent proposal of federated
split task-agnostic (FeSTA) learning tries to reconcile the distinct merits of
FL and SL by enabling the multi-task collaboration between participants through
Vision Transformer (ViT) architecture, but they suffer from higher
communication overhead. To address this, here we present a multi-task
distributed learning using ViT with random patch permutation. Instead of using
a CNN based head as in FeSTA, p-FeSTA adopts a randomly permuting simple patch
embedder, improving the multi-task learning performance without sacrificing
privacy. Experimental results confirm that the proposed method significantly
enhances the benefit of multi-task collaboration, communication efficiency, and
privacy preservation, shedding light on practical multi-task distributed
learning in the field of medical imaging.
