  Federated learning (FL), as an emerging artificial intelligence (AI)
approach, enables decentralized model training across multiple devices without
exposing their local training data. FL has been increasingly gaining popularity
in both academia and industry. While research works have been proposed to
improve the fault tolerance of FL, the real impact of unreliable devices (e.g.,
dropping out, misconfiguration, poor data quality) in real-world applications
is not fully investigated. We carefully chose two representative, real-world
classification problems with a limited numbers of clients to better analyze FL
fault tolerance. Contrary to the intuition, simple FL algorithms can perform
surprisingly well in the presence of unreliable clients.
