FLoBC: A Decentralized Blockchain-Based Federated Learning Framework

TL;DR

FLoBC introduces a blockchain-based decentralized federated learning framework that enables distributed model training with flexible architecture, ensuring reliable operation and demonstrating its feasibility through experimental analysis.

Contribution

The paper presents a novel blockchain-based framework for decentralized federated learning, accommodating any gradient descent-compatible model and analyzing system performance factors.

Findings

Decentralized federated learning is feasible with blockchain technology.

System performance depends on trainer-validator ratios and reward policies.

FLoBC can serve as an experimental platform for decentralized ML systems.

Abstract

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.