SureFED: Robust Federated Learning via Uncertainty-Aware Inward and Outward Inspection
arxiv(2023)
摘要
In this work, we introduce SureFED, a novel framework for byzantine robust
federated learning. Unlike many existing defense methods that rely on
statistically robust quantities, making them vulnerable to stealthy and
colluding attacks, SureFED establishes trust using the local information of
benign clients. SureFED utilizes an uncertainty aware model evaluation and
introspection to safeguard against poisoning attacks. In particular, each
client independently trains a clean local model exclusively using its local
dataset, acting as the reference point for evaluating model updates. SureFED
leverages Bayesian models that provide model uncertainties and play a crucial
role in the model evaluation process. Our framework exhibits robustness even
when the majority of clients are compromised, remains agnostic to the number of
malicious clients, and is well-suited for non-IID settings. We theoretically
prove the robustness of our algorithm against data and model poisoning attacks
in a decentralized linear regression setting. Proof-of Concept evaluations on
benchmark image classification data demonstrate the superiority of SureFED over
the state of the art defense methods under various colluding and non-colluding
data and model poisoning attacks.
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learning,peer-to-peer
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