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Model poisoning attacks greatly jeopardize the application of federatedlearning (FL). The effectiveness of existing defenses is susceptible to thelatest model poisoning attacks, leading to a decrease in prediction accuracy.Besides, these defenses are intractable to distinguish benign outliers frommalicious gradients, which further compromises the model generalization. Inthis work, we propose a novel proactive defense named RECESS against modelpoisoning attacks. Different from the passive analysis in previous defenses,RECESS proactively queries each participating client with a delicatelyconstructed aggregation gradient, accompanied by the detection of maliciousclients according to their responses with higher accuracy. Furthermore, RECESSuses a new trust scoring mechanism to robustly aggregate gradients. Unlikeprevious methods that score each iteration, RECESS considers clients'performance correlation across multiple iterations to estimate the trust score,substantially increasing fault tolerance. Finally, we extensively evaluateRECESS on typical model architectures and four datasets under various settings.We also evaluated the defensive effectiveness against other types of poisoningattacks, the sensitivity of hyperparameters, and adaptive adversarial attacks.Experimental results show the superiority of RECESS in terms of reducingaccuracy loss caused by the latest model poisoning attacks over five classicand two state-of-the-art defenses.

RECESS Vaccine for Federated Learning: Proactive Defense Against Model Poisoning Attacks