FLoBC: A Decentralized Blockchain-Based Federated Learning Framework
Mohamed Ghanem, Fadi Dawoud, Habiba Gamal, Eslam Soliman, Hossam, Sharara, Tamer El-Batt

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…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Stochastic Gradient Optimization Techniques · Age of Information Optimization
