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.
View on arXiv@article{digregorio2025_2506.02679,
title={ Poster: FedBlockParadox -- A Framework for Simulating and Securing Decentralized Federated Learning },
author={ Gabriele Digregorio and Francesco Bleggi and Federico Caroli and Michele Carminati and Stefano Zanero and Stefano Longari },
journal={arXiv preprint arXiv:2506.02679},
year={ 2025 }
}