While recent works have indicated that federated learning (FL) is vulnerable to poisoning attacks by compromised clients, we show that these works make a number of unrealistic assumptions and arrive at somewhat misleading conclusions. For instance, they often use impractically high percentages of compromised clients or assume unrealistic capabilities for the adversary. We perform the first critical analysis of poisoning attacks under practical production FL environments by carefully characterizing the set of realistic threat models and adversarial capabilities. Our findings are rather surprising: contrary to the established belief, we show that FL, even without any defenses, is highly robust in practice. In fact, we go even further and propose novel, state-of-the-art poisoning attacks under two realistic threat models, and show via an extensive set of experiments across three benchmark datasets how (in)effective poisoning attacks are, especially when simple defense mechanisms are used. We correct previous misconceptions and give concrete guidelines that we hope will encourage our community to conduct more accurate research in this space and build stronger (and more realistic) attacks and defenses.
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