  Aiming at privacy preservation, Federated Learning (FL) is an emerging
machine learning approach enabling model training on decentralized devices or
data sources. The learning mechanism of FL relies on aggregating parameter
updates from individual clients. However, this process may pose a potential
security risk due to the presence of malicious devices. Existing solutions are
either costly due to the use of compute-intensive technology, or restrictive
for reasons of strong assumptions such as the prior knowledge of the number of
attackers and how they attack. Few methods consider both privacy constraints
and uncertain attack scenarios. In this paper, we propose a robust FL approach
based on the credibility management scheme, called Fed-Credit. Unlike previous
studies, our approach does not require prior knowledge of the nodes and the
data distribution. It maintains and employs a credibility set, which weighs the
historical clients' contributions based on the similarity between the local
models and global model, to adjust the global model update. The subtlety of
Fed-Credit is that the time decay and attitudinal value factor are incorporated
into the dynamic adjustment of the reputation weights and it boasts a
computational complexity of O(n) (n is the number of the clients). We conducted
extensive experiments on the MNIST and CIFAR-10 datasets under 5 types of
attacks. The results exhibit superior accuracy and resilience against
adversarial attacks, all while maintaining comparatively low computational
complexity. Among these, on the Non-IID CIFAR-10 dataset, our algorithm
exhibited performance enhancements of 19.5% and 14.5%, respectively, in
comparison to the state-of-the-art algorithm when dealing with two types of
data poisoning attacks.
