  Self-supervised learning in federated learning paradigm has been gaining a
lot of interest both in industry and research due to the collaborative learning
capability on unlabeled yet isolated data. However, self-supervised based
federated learning strategies suffer from performance degradation due to label
scarcity and diverse data distributions, i.e., data heterogeneity. In this
paper, we propose the SelfFed framework for Internet of Medical Things (IoMT).
Our proposed SelfFed framework works in two phases. The first phase is the
pre-training paradigm that performs augmentive modeling using Swin Transformer
based encoder in a decentralized manner. The first phase of SelfFed framework
helps to overcome the data heterogeneity issue. The second phase is the
fine-tuning paradigm that introduces contrastive network and a novel
aggregation strategy that is trained on limited labeled data for a target task
in a decentralized manner. This fine-tuning stage overcomes the label scarcity
problem. We perform our experimental analysis on publicly available medical
imaging datasets and show that our proposed SelfFed framework performs better
when compared to existing baselines concerning non-independent and identically
distributed (IID) data and label scarcity. Our method achieves a maximum
improvement of 8.8% and 4.1% on Retina and COVID-FL datasets on non-IID
dataset. Further, our proposed method outperforms existing baselines even when
trained on a few (10%) labeled instances.
