Author

Hanyuan Zheng

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Book Open access Aug 2026

FedPurify: Knowledge-Preserving Backdoor Defense with Data-Free Purification in Federated Learning

FedPurify is a framework that performs post-training data-free purification to remove malicious backdoors while preserving task-relevant knowledge in FL, and combines contrastive feature alignment with knowledge-preserving self-distillation to remove backdoor effects while preserving benign task performance.

Baolu Xue, Hanyuan Zheng, Tian-Xing Man et al. · Proceedings of the 32nd ACM... · 0 citations

Book Open access Aug 2026

FedPurify: Knowledge-Preserving Backdoor Defense with Data-Free Purification in Federated Learning

Federated Learning (FL) facilitates distributed training while preserving privacy, yet remains highly vulnerable to backdoor attacks. Existing defenses primarily address these threats via robust aggregation, which reduces the influence of malicious contributions (e.g., pruning, reweighting), or through detection-based...

Baolu Xue, Hanyuan Zheng, T. Man et al. · Proceedings of the 32nd ACM... · 0 citations