MULTI-FLGANs: Multi Distributed Adversarial Networks for Non-IID distributed datasets
A. Amalan (TU Delft - Electrical Engineering, Mathematics and Computer Science)
K. Liang – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
R. Wang – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
J. Urbano Merino – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Federated learning is an emerging concept in the domain of distributed machine learning. This concept has enabled GANs to benefit from the rich distributed training data while preserving privacy However,in a non-iid setting, current federated GAN architectures are unstable, struggling to learn the distinct features and vulnerable to mode collapse. In this paper, we propose a novel architecture MULTIFLGAN to solve the problem of low-quality images, mode collapse and instability for non-iid datasets. Our results show that MULTI-FLGAN is four times as stable and performant (i.e. high inception score) on average over 20 clients compared to baseline FLGAN.