  Federated learning---multi-party, distributed learning in a decentralized
environment---is vulnerable to model poisoning attacks, even more so than
centralized learning approaches. This is because malicious clients can collude
and send in carefully tailored model updates to make the global model
inaccurate. This motivated the development of Byzantine-resilient federated
learning algorithms, such as Krum, Bulyan, FABA, and FoolsGold. However, a
recently developed untargeted model poisoning attack showed that all prior
defenses can be bypassed. The attack uses the intuition that simply by changing
the sign of the gradient updates that the optimizer is computing, for a set of
malicious clients, a model can be diverted from the optima to increase the test
error rate. In this work, we develop TESSERACT---a defense against this
directed deviation attack, a state-of-the-art model poisoning attack. TESSERACT
is based on a simple intuition that in a federated learning setting, certain
patterns of gradient flips are indicative of an attack. This intuition is
remarkably stable across different learning algorithms, models, and datasets.
TESSERACT assigns reputation scores to the participating clients based on their
behavior during the training phase and then takes a weighted contribution of
the clients. We show that TESSERACT provides robustness against even a
white-box version of the attack.
