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arXiv · 2506.02679

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

Gabriele Digregorio·Francesco Bleggi·Federico Caroli·Michele Carminati·Stefano Zanero·Stefano Longari
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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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Gabriele Digregorio, Francesco Bleggi, Federico Caroli, Michele Carminati, Stefano Zanero, Stefano Longari. 2025-06-03. Poster: FedBlockParadox -- A Framework for Simulating and Securing Decentralized Federated Learning. https://arxiv.org/abs/2506.02679

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