Data security and user privacy-issue have become an important field. As federated learning (FL) could solve the problems from data security and privacy-issue, it starts to be applied in many different applied machine learning tasks. However, FL does not verify the quality of the data from different parties in the system. Hence, the low-quality datasets with fewer common entities can be cotrained with others. This could result in a huge amount of computing-resources waste, and the attack on the FL model from malicious clients as federal members. To solve this problem, this article proposes a secure member selection strategy (SMSS), which can evaluate the data qualities of members before training. With SMSS, only datasets share more common entities than a certain threshold can be selected for learning, whereas malicious clients with fewer common objects cannot acquire any information about the model. This article implements SMSS, and evaluate its performance via several extensive experiments. Experimental results demonstrate that SMSS is safe, efficient, and effective.

中文翻译：

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SMSS：联邦学习中的安全成员选择策略


数据安全和用户隐私问题已成为一个重要领域。由于联邦学习（FL）可以解决数据安全和隐私问题，因此它开始应用于许多不同的应用机器学习任务。然而，FL 并不验证系统中不同各方的数据质量。因此，常见实体较少的低质量数据集可以与其他数据集共同训练。这可能会导致大量的计算资源浪费，以及作为联邦成员的恶意客户端对 FL 模型的攻击。为了解决这个问题，本文提出了一种安全成员选择策略（SMSS），可以在训练前评估成员的数据质量。使用 SMSS，只有共享超过一定阈值的公共实体的数据集才能被选择进行学习，而具有较少公共对象的恶意客户端无法获取有关模型的任何信息。本文实现了 SMSS，并通过多次广泛的实验评估了其性能。实验结果表明SMSS安全、高效、有效。