TL;DR
It is demonstrated that FHE-enabled models remain susceptible to both evasion and backdoor attacks, underscoring the need for stronger defenses in encrypted ML systems.
This survey examines KD-based FL through a privacy and security lens, and provides a precise framework for evaluating what KD-FL methods actually guarantee and what they only make harder for an adversary.
Experimental results indicate that TMI-VFL achieves an effective trade-off between privacy protection and model utility, providing a practical solution for secure VFL.
To mitigate the attacks of transferable adversarial examples, a defense mechanism stemming from the transferability of model robustness by adversarial training is designed, gaining insights into adversarial examples and the vulnerability of federated learning systems.
The results of the experiments show that the hardware-accelerated implementation of the FHE primitives speeds up the encrypted gradient descent algorithm significantly.
Federated learning (FL) enables collaborative model training without sharing raw data, but remains vulnerable to Byzantine attacks and privacy leakage. Existing privacy-preserving robust FL schemes suffer from prohibitive computation and communication overheads, particularly on resourceconstrained clients. To address t...
BackDFL is presented, a unified benchmark for systematically evaluating DFL under realistic and adaptive backdoor attacks, and demonstrates that both state-of-the-art Byzantine-robust DFL methods and adapted FL backdoor defenses fail under modest malicious participation rates, especially in heterogeneous settings.
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