FLoBC: A Decentralized Blockchain-Based Federated Learning Framework
This work addresses the need for scalable and decentralized machine learning systems, but it appears incremental as it builds on existing federated learning and blockchain concepts without claiming major breakthroughs.
The authors tackled the problem of building a decentralized federated learning system using blockchain technology, showing that it is a feasible alternative to centralized architectures by experimentally analyzing factors like trainer-to-validator ratio and reward-penalty policies.
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.