  The rapid expansion of data worldwide invites the need for more distributed
solutions in order to apply machine learning on a much wider scale. The
resultant distributed learning systems can have various degrees of
centralization. In this work, we demonstrate our solution FLoBC for building a
generic decentralized federated learning system using blockchain technology,
accommodating any machine learning model that is compatible with gradient
descent optimization. We present our system design comprising the two
decentralized actors: trainer and validator, alongside our methodology for
ensuring reliable and efficient operation of said system. Finally, we utilize
FLoBC as an experimental sandbox to compare and contrast the effects of
trainer-to-validator ratio, reward-penalty policy, and model synchronization
schemes on the overall system performance, ultimately showing by example that a
decentralized federated learning system is indeed a feasible alternative to
more centralized architectures.
