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

• URL: http://arxiv.org/abs/2506.02679v1
• Date: Tue, 03 Jun 2025 09:25:06 GMT
• Title: Poster: FedBlockParadox -- A Framework for Simulating and Securing Decentralized Federated Learning
• Authors: Gabriele Digregorio, Francesco Bleggi, Federico Caroli, Michele Carminati, Stefano Zanero, Stefano Longari,
• Abstract summary: FedBlockParadox is a modular framework for modeling and evaluating decentralized federated learning systems built on blockchain technologies.<n>It supports multiple consensus protocols, validation methods, aggregation strategies, and adversarial attack models.<n>By enabling controlled experiments, FedBlockParadox provides a valuable resource for researchers developing secure, decentralized learning solutions.
• Score: 5.585625844344932
• License: http://creativecommons.org/licenses/by/4.0/
• 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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