  This work addresses a new problem that learns generative adversarial networks
(GANs) from multiple data collections that are each i) owned separately by
different clients and ii) drawn from a non-identical distribution that
comprises different classes. Given such non-iid data as input, we aim to learn
a distribution involving all the classes input data can belong to, while
keeping the data decentralized in each client storage. Our key contribution to
this end is a new decentralized approach for learning GANs from non-iid data
called Forgiver-First Update (F2U), which a) asks clients to train an
individual discriminator with their own data and b) updates a generator to fool
the most `forgiving' discriminators who deem generated samples as the most
real. Our theoretical analysis proves that this updating strategy allows the
decentralized GAN to achieve a generator's distribution with all the input
classes as its global optimum based on f-divergence minimization. Moreover, we
propose a relaxed version of F2U called Forgiver-First Aggregation (F2A) that
performs well in practice, which adaptively aggregates the discriminators while
emphasizing forgiving ones. Our empirical evaluations with image generation
tasks demonstrated the effectiveness of our approach over state-of-the-art
decentralized learning methods.
