  Federated learning (FL) is a distributed machine learning paradigm where
enormous scattered clients (e.g. mobile devices or IoT devices) collaboratively
train a model under the orchestration of a central server (e.g. service
provider), while keeping the training data decentralized. Unfortunately, FL is
susceptible to a variety of attacks, including backdoor attack, which is made
substantially worse in the presence of malicious attackers. Most of algorithms
usually assume that the malicious at tackers no more than benign clients or the
data distribution is independent identically distribution (IID). However, no
one knows the number of malicious attackers and the data distribution is
usually non identically distribution (Non-IID). In this paper, we propose
RFLBAT which utilizes principal component analysis (PCA) technique and Kmeans
clustering algorithm to defend against backdoor attack. Our algorithm RFLBAT
does not bound the number of backdoored attackers and the data distribution,
and requires no auxiliary information outside of the learning process. We
conduct extensive experiments including a variety of backdoor attack types.
Experimental results demonstrate that RFLBAT outperforms the existing
state-of-the-art algorithms and is able to resist various backdoor attack
scenarios including different number of attackers (DNA), different Non-IID
scenarios (DNS), different number of clients (DNC) and distributed backdoor
attack (DBA).
