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

arXiv: https://arxiv.org/abs/2310.05431 · HTML: https://mlsanity.com/paper/2310.05431/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2023.

## Built on by (most cited first, 4 of 4)

- [Bant: Byzantine Antidote via Trial Function and Trust Scores](https://mlsanity.com/paper/2505.07614/) (2025)
- [FedDTPT: Federated Discrete and Transferable Prompt Tuning for Black-Box Large Language Models](https://mlsanity.com/paper/2411.00985/) (2024)
- [DBA-DFL: Towards Distributed Backdoor Attacks with Network Detection in Decentralized Federated Learning](https://mlsanity.com/paper/2501.15005/) (2025)
- [From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning](https://mlsanity.com/paper/2606.10595/) (2026)

## Abstract (arXiv)

Model poisoning attacks greatly jeopardize the application of federated learning (FL). The effectiveness of existing defenses is susceptible to the latest model poisoning attacks, leading to a decrease in prediction accuracy. Besides, these defenses are intractable to distinguish benign outliers from malicious gradients, which further compromises the model generalization. In this work, we propose a novel proactive defense named RECESS against model poisoning attacks. Different from the passive analysis in previous defenses, RECESS proactively queries each participating client with a delicately constructed aggregation gradient, accompanied by the detection of malicious clients according to their responses with higher accuracy. Furthermore, RECESS uses a new trust scoring mechanism to robustly aggregate gradients. Unlike previous 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 evaluate RECESS on typical model architectures and four datasets under various settings. We also evaluated the defensive effectiveness against other types of poisoning attacks, the sensitivity of hyperparameters, and adaptive adversarial attacks. Experimental results show the superiority of RECESS in terms of reducing accuracy loss caused by the latest model poisoning attacks over five classic and two state-of-the-art defenses.