  Federated Learning (FL) is becoming a popular paradigm for leveraging
distributed data and preserving data privacy. However, due to the distributed
characteristic, FL systems are vulnerable to Byzantine attacks that compromised
clients attack the global model by uploading malicious model updates. With the
development of layer-level and parameter-level fine-grained attacks, the
attacks' stealthiness and effectiveness have been significantly improved. The
existing defense mechanisms solely analyze the model-level statistics of
individual model updates uploaded by clients to mitigate Byzantine attacks,
which are ineffective against fine-grained attacks due to unawareness or
overreaction. To address this problem, we propose SkyMask, a new
attack-agnostic robust FL system that firstly leverages fine-grained learnable
masks to identify malicious model updates at the parameter level. Specifically,
the FL server freezes and multiplies the model updates uploaded by clients with
the parameter-level masks, and trains the masks over a small clean dataset
(i.e., root dataset) to learn the subtle difference between benign and
malicious model updates in a high-dimension space. Our extensive experiments
involve different models on three public datasets under state-of-the-art (SOTA)
attacks, where the results show that SkyMask achieves up to 14% higher testing
accuracy compared with SOTA defense strategies under the same attacks and
successfully defends against attacks with malicious clients of a high fraction
up to 80%. Code is available at https://github.com/KoalaYan/SkyMask.
