Poster: FedBlockParadox -- A Framework for Simulating and Securing Decentralized Federated Learning

TL;DR

FedBlockParadox is an open-source, modular framework designed to simulate and evaluate the robustness of decentralized federated learning systems built on blockchain technology against various adversarial attacks.

Contribution

It introduces a flexible, extensible tool for modeling and testing the security and resilience of blockchain-based federated learning systems under adversarial conditions.

Findings

Supports multiple consensus protocols and attack models

Enables controlled experiments for robustness evaluation

Facilitates development of secure decentralized learning solutions

Abstract

A significant body of research in decentralized federated learning focuses on combining the privacy-preserving properties of federated learning with the resilience and transparency offered by blockchain-based systems. While these approaches are promising, they often lack flexible tools to evaluate system robustness under adversarial conditions. To fill this gap, we present FedBlockParadox, a modular framework for modeling and evaluating decentralized federated learning systems built on blockchain technologies, with a focus on resilience against a broad spectrum of adversarial attack scenarios. It supports multiple consensus protocols, validation methods, aggregation strategies, and configurable attack models. By enabling controlled experiments, FedBlockParadox provides a valuable resource for researchers developing secure, decentralized learning solutions. The framework is open-source and built to be extensible by the community.