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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.02679 |
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| _version_ | 1866909634926739456 |
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| author | Digregorio, Gabriele Bleggi, Francesco Caroli, Federico Carminati, Michele Zanero, Stefano Longari, Stefano |
| author_facet | Digregorio, Gabriele Bleggi, Francesco Caroli, Federico Carminati, Michele Zanero, Stefano Longari, Stefano |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02679 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Poster: FedBlockParadox -- A Framework for Simulating and Securing Decentralized Federated Learning Digregorio, Gabriele Bleggi, Francesco Caroli, Federico Carminati, Michele Zanero, Stefano Longari, Stefano Cryptography and Security 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. |
| title | Poster: FedBlockParadox -- A Framework for Simulating and Securing Decentralized Federated Learning |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2506.02679 |