【Author】
Liu, Yi; Peng, Jialiang; Kang, Jiawen; Iliyasu, Abdullah M.; Niyato, Dusit; Abd El-Latif, Ahmed A.
【Source】IEEE WIRELESS COMMUNICATIONS
【Abstract】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.
【摘要】联邦学习 (FL) 最近被提议作为一种新兴范式，使用分布式训练数据集构建机器学习模型，这些数据集在 5G 网络中的不同设备上本地存储和维护，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，有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进行，导致全局模型的构建失败或 FL 模型的隐私泄露。因此，开发安全防御手段对于 FL 至关重要。在本文中，我们提出了一个基于区块链的安全 FL 框架来创建智能合约并防止恶意或不可靠的参与者参与 FL。这样做时，中央聚合器通过自动执行智能合约来识别恶意和不可靠的参与者，以防御中毒攻击。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，所提出的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。
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