Targeted model poisoning attacks pose a significant threat to federated learning systems. Recent studies show that edge-case targeted attacks, which target a small fraction of the input space are nearly impossible to counter using existing fixed defense strategies. In this paper, we strive to design a learned-defense strategy against such attacks, using a small defense dataset. The defense dataset can be collected by the central authority of the federated learning task, and should contain a mix of poisoned and clean examples. The proposed framework, LearnDefend, estimates the probability of a client update being malicious. The examples in defense dataset need not be pre-marked as poisoned or clean. We also learn a poisoned data detector model which can be used to mark each example in the defense dataset as clean or poisoned. We estimate the poisoned data detector and the client importance models in a coupled optimization approach. Our experiments demonstrate that LearnDefend is capable of defending against state-of-the-art attacks where existing fixed defense strategies fail. We also show that LearnDefend is robust to size and noise in the marking of clean examples in the defense dataset.

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