  In federated learning (FL), model aggregation has been widely adopted for
data privacy. In recent years, assigning different weights to local models has
been used to alleviate the FL performance degradation caused by differences
between local datasets. However, when various defects make the FL process
unreliable, most existing FL approaches expose weak robustness. In this paper,
we propose the DEfect-AwaRe federated soft actor-critic (DearFSAC) to
dynamically assign weights to local models to improve the robustness of FL. The
deep reinforcement learning algorithm soft actor-critic is adopted for
near-optimal performance and stable convergence. Besides, an auto-encoder is
trained to output low-dimensional embedding vectors that are further utilized
to evaluate model quality. In the experiments, DearFSAC outperforms three
existing approaches on four datasets for both independent and identically
distributed (IID) and non-IID settings under defective scenarios.
