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

3 June 2025
Gabriele Digregorio
Francesco Bleggi
Federico Caroli
Michele Carminati
S. Zanero
Stefano Longari
    FedML
ArXiv (abs)PDFHTML
Main:4 Pages
1 Figures
Bibliography:2 Pages
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.

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@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 }
}
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