  Federated learning (FL) is revolutionizing how we learn from data. With its
growing popularity, it is now being used in many safety-critical domains such
as autonomous vehicles and healthcare. Since thousands of participants can
contribute in this collaborative setting, it is, however, challenging to ensure
security and reliability of such systems. This highlights the need to design FL
systems that are secure and robust against malicious participants' actions
while also ensuring high utility, privacy of local data, and efficiency. In
this paper, we propose a novel FL framework dubbed as FLShield that utilizes
benign data from FL participants to validate the local models before taking
them into account for generating the global model. This is in stark contrast
with existing defenses relying on server's access to clean datasets -- an
assumption often impractical in real-life scenarios and conflicting with the
fundamentals of FL. We conduct extensive experiments to evaluate our FLShield
framework in different settings and demonstrate its effectiveness in thwarting
various types of poisoning and backdoor attacks including a defense-aware one.
FLShield also preserves privacy of local data against gradient inversion
attacks.
