  Swarm learning (SL) is an emerging promising decentralized machine learning
paradigm and has achieved high performance in clinical applications. SL solves
the problem of a central structure in federated learning by combining edge
computing and blockchain-based peer-to-peer network. While there are promising
results in the assumption of the independent and identically distributed (IID)
data across participants, SL suffers from performance degradation as the degree
of the non-IID data increases. To address this problem, we propose a generative
augmentation framework in swarm learning called SL-GAN, which augments the
non-IID data by generating the synthetic data from participants. SL-GAN trains
generators and discriminators locally, and periodically aggregation via a
randomly elected coordinator in SL network. Under the standard assumptions, we
theoretically prove the convergence of SL-GAN using stochastic approximations.
Experimental results demonstrate that SL-GAN outperforms state-of-art methods
on three real world clinical datasets including Tuberculosis, Leukemia,
COVID-19.
