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Found 3 papers, 1 papers with code
Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples
In response to these challenges, we propose Genetic Evolution-Nurtured Adversarial Fine-tuning (Gen-AF), a two-stage adversarial fine-tuning approach aimed at enhancing the robustness of downstream models.
Revisiting Gradient Pruning: A Dual Realization for Defending against Gradient Attacks
To mitigate the weaknesses of existing solutions, we propose a novel defense method, Dual Gradient Pruning (DGP), based on gradient pruning, which can improve communication efficiency while preserving the utility and privacy of CL.
MISA: Unveiling the Vulnerabilities in Split Federated Learning
This attack unveils the vulnerabilities in SFL, challenging the conventional belief that SFL is robust against poisoning attacks.
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