  Despite the potential of federated learning, it is known to be vulnerable to
backdoor attacks. Many robust federated aggregation methods are proposed to
reduce the potential backdoor risk. However, they are mainly validated in the
CV field. In this paper, we find that NLP backdoors are hard to defend against
than CV, and we provide a theoretical analysis that the malicious update
detection error probabilities are determined by the relative backdoor
strengths. NLP attacks tend to have small relative backdoor strengths, which
may result in the failure of robust federated aggregation methods for NLP
attacks. Inspired by the theoretical results, we can choose some dimensions
with higher backdoor strengths to settle this issue. We propose a novel
federated aggregation algorithm, Dim-Krum, for NLP tasks, and experimental
results validate its effectiveness.
