  Blockchain promises to enhance distributed machine learning (ML) approaches
such as federated learning (FL) by providing further decentralization,
security, immutability, and trust, which are key properties for enabling
collaborative intelligence in next-generation applications. Nonetheless, the
intrinsic decentralized operation of peer-to-peer (P2P) blockchain nodes leads
to an uncharted setting for FL, whereby the concepts of FL round and global
model become meaningless, as devices' synchronization is lost without the
figure of a central orchestrating server. In this paper, we study the practical
implications of outsourcing the orchestration of FL to a democratic setting
such as in a blockchain. In particular, we focus on the effects that model
staleness and inconsistencies, endorsed by blockchains' modus operandi, have on
the training procedure held by FL devices asynchronously. Using simulation, we
evaluate the blockchained FL operation by applying two different ML models
(ranging from low to high complexity) on the well-known MNIST and CIFAR-10
datasets, respectively, and focus on the accuracy and timeliness of the
solutions. Our results show the high impact of model inconsistencies on the
accuracy of the models (up to a ~35% decrease in prediction accuracy), which
underscores the importance of properly designing blockchain systems based on
the characteristics of the underlying FL application.
