  Data heterogeneity has been identified as one of the key features in
federated learning but often overlooked in the lens of robustness to
adversarial attacks. This paper focuses on characterizing and understanding its
impact on backdooring attacks in federated learning through comprehensive
experiments using synthetic and the LEAF benchmarks. The initial impression
driven by our experimental results suggests that data heterogeneity is the
dominant factor in the effectiveness of attacks and it may be a redemption for
defending against backdooring as it makes the attack less efficient, more
challenging to design effective attack strategies, and the attack result also
becomes less predictable. However, with further investigations, we found data
heterogeneity is more of a curse than a redemption as the attack effectiveness
can be significantly boosted by simply adjusting the client-side backdooring
timing. More importantly,data heterogeneity may result in overfitting at the
local training of benign clients, which can be utilized by attackers to
disguise themselves and fool skewed-feature based defenses. In addition,
effective attack strategies can be made by adjusting attack data distribution.
Finally, we discuss the potential directions of defending the curses brought by
data heterogeneity. The results and lessons learned from our extensive
experiments and analysis offer new insights for designing robust federated
learning methods and systems
