  Federated learning is a novel framework that enables resource-constrained
edge devices to jointly learn a model, which solves the problem of data
protection and data islands. However, standard federated learning is vulnerable
to Byzantine attacks, which will cause the global model to be manipulated by
the attacker or fail to converge. On non-iid data, the current methods are not
effective in defensing against Byzantine attacks. In this paper, we propose a
Byzantine-robust framework for federated learning via credibility assessment on
non-iid data (BRCA). Credibility assessment is designed to detect Byzantine
attacks by combing adaptive anomaly detection model and data verification.
Specially, an adaptive mechanism is incorporated into the anomaly detection
model for the training and prediction of the model. Simultaneously, a unified
update algorithm is given to guarantee that the global model has a consistent
direction. On non-iid data, our experiments demonstrate that the BRCA is more
robust to Byzantine attacks compared with conventional methods
