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Privacy and annotation bottlenecks are two major issues that profoundlyaffect the practicality of machine learning-based medical image analysis.Although significant progress has been made in these areas, these issues arenot yet fully resolved. In this paper, we seek to tackle these concerns head-onand systematically explore the applicability of non-contrastive self-supervisedlearning (SSL) algorithms under federated learning (FL) simulations for medicalimage analysis. We conduct thorough experimentation of recently proposedstate-of-the-art non-contrastive frameworks under standard FL setups. With theSoTA Contrastive Learning algorithm, SimCLR as our comparative baseline, webenchmark the performances of our 4 chosen non-contrastive algorithms undernon-i.i.d. data conditions and with a varying number of clients. We present aholistic evaluation of these techniques on 6 standardized medical imagingdatasets. We further analyse different trends inferred from the findings of ourresearch, with the aim to find directions for further research based on ours.To the best of our knowledge, ours is the first to perform such a thoroughanalysis of federated self-supervised learning for medical imaging. All of oursource code will be made public upon acceptance of the paper.

An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysis