Federated Learning (FL) has emerged as a key paradigm for building Trustworthy AI systems by enabling privacy-preserving, decentralized model training. However, FL is highly susceptible to adversarial attacks that compromise model integrity and data confidentiality, a vulnerability exacerbated by the fact that conventional data inspection methods are incompatible with its decentralized design. While integrating FL with Blockchain technology has been proposed to address some limitations, its potential for mitigating adversarial attacks remains largely unexplored. This paper introduces Resilient Federated Chain (RFC), a novel blockchain-enabled FL framework designed specifically to enhance resilience against such threats. RFC builds upon the existing Proof of Federated Learning architecture by repurposing the redundancy of its Pooled Mining mechanism as an active defense layer that can be combined with robust aggregation rules. Furthermore, the framework introduces a flexible evaluation function in its consensus mechanism, allowing for adaptive defense against different attack strategies. Extensive experimental evaluation on image classification tasks under various adversarial scenarios, demonstrates that RFC significantly improves robustness compared to baseline methods, providing a viable solution for securing decentralized learning environments.

翻译：联邦学习已成为构建可信人工智能系统的关键范式，通过实现隐私保护的分布式模型训练。然而，联邦学习极易受到损害模型完整性和数据机密性的对抗性攻击，这一脆弱性因其去中心化设计与传统数据检测方法不兼容而加剧。尽管已有研究提出将联邦学习与区块链技术结合以解决部分局限性，但其在缓解对抗性攻击方面的潜力仍未得到充分探索。本文提出弹性联邦链，这是一种新颖的基于区块链的联邦学习框架，专门设计用于增强对此类威胁的抵御能力。弹性联邦链在现有联邦学习证明架构的基础上，将其池化挖矿机制的冗余性重新定位为可与鲁棒聚合规则结合的主动防御层。此外，该框架在其共识机制中引入了灵活的评估函数，从而能够针对不同攻击策略实现自适应防御。在多种对抗场景下对图像分类任务进行的广泛实验评估表明，弹性联邦链相较于基线方法显著提升了鲁棒性，为保护去中心化学习环境提供了一种可行的解决方案。