  Federated Contrastive Learning (FCL) is an emerging privacy-preserving
paradigm in distributed learning for unlabeled data. In FCL, distributed
parties collaboratively learn a global encoder with unlabeled data, and the
global encoder could be widely used as a feature extractor to build models for
many downstream tasks. However, FCL is also vulnerable to many security threats
(e.g., backdoor attacks) due to its distributed nature, which are seldom
investigated in existing solutions. In this paper, we study the backdoor attack
against FCL as a pioneer research, to illustrate how backdoor attacks on
distributed local clients act on downstream tasks. Specifically, in our system,
malicious clients can successfully inject a backdoor into the global encoder by
uploading poisoned local updates, thus downstream models built with this global
encoder will also inherit the backdoor. We also investigate how to inject
backdoors into multiple downstream models, in terms of two different backdoor
attacks, namely the \textit{centralized attack} and the \textit{decentralized
attack}. Experiment results show that both the centralized and the
decentralized attacks can inject backdoors into downstream models effectively
with high attack success rates. Finally, we evaluate two defense methods
against our proposed backdoor attacks in FCL, which indicates that the
decentralized backdoor attack is more stealthy and harder to defend.
