在联合学习中，每个参与者通过自己的数据列举其本地模型，并通过聚合来自这些参与者的模型更新来在可信服务器上形成全局模型。由于服务器对参与者的培训程序没有影响和可见性以确保隐私，因此全球模型变得容易受到数据中毒和模型中毒等攻击的影响。虽然最近已经提出了许多防御算法来解决这些攻击，但它们往往会使强烈的假设与联邦学习的性质相吻，例如非IID数据集。此外，它们大多缺乏全面的实验分析。在这项工作中，我们提出了一种称为Barfed的防御算法，不会对数据分布，更新参与者的相似性或恶意参与者的比率作出任何假设。 Barfed主要考虑基于与全局模型的距离的模型架构的每个层的参与者更新的异常状态。因此，没有任何异常层的参与者都参与了模型聚合。我们在许多场所进行广泛的实验，并表明该方法为不同攻击提供了强大的防御。

This work addresses an alternative approach for query expansion (QE) using a generative adversarial network (GAN) to enhance the effectiveness of information search in e-commerce. We propose a modified QE conditional GAN (mQE-CGAN) framework, which resolves keywords by expanding the query with a synthetically generated query that proposes semantic information from text input. We train a sequence-to-sequence transformer model as the generator to produce keywords and use a recurrent neural network model as the discriminator to classify an adversarial output with the generator. With the modified CGAN framework, various forms of semantic insights gathered from the query document corpus are introduced to the generation process. We leverage these insights as conditions for the generator model and discuss their effectiveness for the query expansion task. Our experiments demonstrate that the utilization of condition structures within the mQE-CGAN framework can increase the semantic similarity between generated sequences and reference documents up to nearly 10% compared to baseline models