EBLC

Compression Footprints as Security Signals: Defending Federated Learning Against Model Poisoning

壓縮足跡化身安全訊號：利用破壞性壓縮抵禦聯邦學習中的模型投毒攻擊

This study introduces CRAFT, a robust aggregation method that repurposes lossy compression distortions in Federated Learning into diagnostic footprints to detect and mitigate model-poisoning attacks.