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2020 – today
- 2024
[c3]Changwei Liu, Louis DiValentin, Aolin Ding, Malek Ben Salem:
Build a Computationally Efficient Strong Defense Against Adversarial Example Attacks. ICISSP 2024: 358-365- [c2]Minxue Tang, Anna Dai, Louis DiValentin, Aolin Ding, Amin Hass, Neil Zhenqiang Gong, Yiran Chen, Hai (Helen) Li:
ModelGuard: Information-Theoretic Defense Against Model Extraction Attacks. USENIX Security Symposium 2024 - [i4]Fernando Vera, Palina Pauliuchenka, Ethan Oh, Bai Chien Kao, Louis DiValentin, David A. Bader:
Profile of Vulnerability Remediations in Dependencies Using Graph Analysis. CoRR abs/2403.04989 (2024) - [i3]Minxue Tang, Yitu Wang, Jingyang Zhang, Louis DiValentin, Aolin Ding, Amin Hass, Yiran Chen, Hai (Helen) Li:
FedProphet: Memory-Efficient Federated Adversarial Training via Theoretic-Robustness and Low-Inconsistency Cascade Learning. CoRR abs/2409.08372 (2024) - 2022
- [i2]Minxue Tang, Jianyi Zhang, Mingyuan Ma, Louis DiValentin, Aolin Ding, Amin Hassanzadeh, Hai Helen Li, Yiran Chen:
FADE: Enabling Large-Scale Federated Adversarial Training on Resource-Constrained Edge Devices. CoRR abs/2209.03839 (2022) - 2021
- [c1]Jingwei Sun, Ang Li, Louis DiValentin, Amin Hassanzadeh, Yiran Chen, Hai Li:
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective. NeurIPS 2021: 12613-12624 - [i1]Jingwei Sun, Ang Li, Louis DiValentin, Amin Hassanzadeh, Yiran Chen, Hai Li:
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective. CoRR abs/2110.13864 (2021)
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