Federated learning (FL), with the growing IoT and edge computing, is seen as a promising solution for applications that are latency- and privacy-aware. However, due to the widespread dispersion of data across many clients, it is challenging to monitor client anomalies caused by malfunctioning devices or unexpected events. The majority of FL solutions now in use concentrate on the classification problem, ignoring situations in which anomaly detection may also necessitate privacy preservation and effectiveness. The system in federated learning is unable to manage the potentially flawed behavior of its clients completely. These behaviors include sharing arbitrary parameter values and causing a delay in convergence since clients are chosen at random without knowing the malfunctioning behavior of the client. Client selection is crucial in terms of the efficiency of the federated learning framework. The challenges such as client drift and handling slow clients with low computational capability are well-studied in FL. However, the detection of anomalous clients either for security or for overall performance in the FL frameworks is hardly studied in the literature. In this paper, we propose an anomaly client detection algorithm to overcome malicious client attacks and client drift in FL frameworks. Instead of random client selection, our proposed method utilizes anomaly client detection to remove clients from the FL framework, thereby enhancing the security and efficiency of the overall system. This proposed method improves the global model convergence in almost 50\% fewer communication rounds compared with widely used random client selection using the MNIST dataset.

翻译：随着物联网和边缘计算的不断发展，联邦学习（FL）被视为对延迟和隐私敏感应用的一种有前景的解决方案。然而，由于数据广泛分散在众多客户端之间，监测由设备故障或意外事件引起的客户端异常具有挑战性。目前大多数联邦学习解决方案集中于分类问题，忽略了异常检测同样可能需要隐私保护和高效性的场景。联邦学习系统无法完全管理其客户端可能存在的缺陷行为。这些行为包括共享任意参数值以及导致收敛延迟，因为客户端是在不了解其故障行为的情况下随机选择的。客户端选择对于联邦学习框架的效率至关重要。诸如客户端漂移和处理计算能力低下的慢速客户端等挑战在联邦学习领域已得到充分研究。然而，针对联邦学习框架中出于安全或整体性能考虑的异常客户端检测，在现有文献中却鲜有研究。本文提出一种异常客户端检测算法，以克服联邦学习框架中的恶意客户端攻击和客户端漂移问题。与随机客户端选择不同，我们提出的方法利用异常客户端检测将客户端从联邦学习框架中移除，从而提升整个系统的安全性和效率。在使用MNIST数据集的实验中，与广泛采用的随机客户端选择方法相比，该方法在减少近50%的通信轮次内实现了全局模型的收敛。