Federated learning is a decentralized machine learning approach where clients train models locally and share model updates to develop a global model. This enables low-resource devices to collaboratively build a high-quality model without requiring direct access to the raw training data. However, despite only sharing model updates, federated learning still faces several privacy vulnerabilities. One of the key threats is membership inference attacks, which target clients' privacy by determining whether a specific example is part of the training set. These attacks can compromise sensitive information in real-world applications, such as medical diagnoses within a healthcare system. Although there has been extensive research on membership inference attacks, a comprehensive and up-to-date survey specifically focused on it within federated learning is still absent. To fill this gap, we categorize and summarize membership inference attacks and their corresponding defense strategies based on their characteristics in this setting. We introduce a unique taxonomy of existing attack research and provide a systematic overview of various countermeasures. For these studies, we thoroughly analyze the strengths and weaknesses of different approaches. Finally, we identify and discuss key future research directions for readers interested in advancing the field.

翻译：联邦学习是一种去中心化的机器学习方法，客户端在本地训练模型并共享模型更新以构建全局模型。这使得资源受限的设备能够协作构建高质量模型，而无需直接访问原始训练数据。然而，尽管仅共享模型更新，联邦学习仍面临多种隐私漏洞。其中关键威胁之一是成员推理攻击，该攻击通过判断特定样本是否属于训练集来侵犯客户端隐私。此类攻击可能危及现实应用中的敏感信息，例如医疗系统中的诊断数据。尽管已有大量关于成员推理攻击的研究，但针对联邦学习场景的全面且最新的专题综述仍属空白。为填补这一空白，我们根据该场景下攻击与防御策略的特性，对其进行了分类与总结。我们提出了现有攻击研究的独特分类体系，并系统性地概述了各类防御措施。针对这些研究，我们深入分析了不同方法的优势与不足。最后，我们为有意推动该领域发展的研究者指明并探讨了未来关键研究方向。