Federated learning (FL) has recently been proposed as an emerging paradigm to build machine learning models using distributed training datasets that are locally stored and maintained on different devices in 5G networks while providing privacy preservation for participants. In FL, the central aggregator accumulates local updates uploaded by participants to update a global model. However, there are two critical security threats: poisoning and membership inference attacks. These attacks may be carried out by malicious or unreliable participants, resulting in the construction failure of global models or privacy leakage of FL models. Therefore, it is crucial for FL to develop security means of defense. In this article, we propose a blockchain-based secure FL framework to create smart contracts and prevent malicious or unreliable participants from being involved in FL. In doing so, the central aggregator recognizes malicious and unreliable participants by automatically executing smart contracts to defend against poisoning attacks. Further, we use local differential privacy techniques to prevent membership inference attacks. Numerical results suggest that the proposed framework can effectively deter poisoning and membership inference attacks, thereby improving the security of FL in 5G networks.

中文翻译：

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适用于5G网络的安全联合学习框架

联合学习（FL）最近被提出为一种新兴的范例，它使用分布式训练数据集构建机器学习模型，该数据集本地存储并维护在5G网络中的不同设备上，同时为参与者提供隐私保护。在FL中，中央聚合器累积参与者上传的本地更新以更新全局模型。但是，存在两个关键的安全威胁：中毒和成员推断攻击。这些攻击可能由恶意或不可靠的参与者执行，导致全局模型的构建失败或FL模型的隐私泄漏。因此，发展国防安全手段至关重要。在这篇文章中，我们提出了一种基于区块链的安全FL框架，以创建智能合约并防止恶意或不可靠的参与者参与FL。这样，中央聚合器通过自动执行智能合约来防御中毒攻击，从而识别恶意和不可靠的参与者。此外，我们使用本地差分隐私技术来防止成员推断攻击。数值结果表明，该框架可以有效地阻止中毒和成员推理攻击，从而提高5G网络中FL的安全性。