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cs.CL2026
Mitigating Gradient Inversion Risks in Language Models via Token Obfuscation
Xinguo Feng, Zhongkui Ma, Zihan Wang +2
Training and fine-tuning large-scale language models largely benefit from collaborative learning, but the approach has been proven vulnerable to gradient inversion attacks (GIAs),…
cs.CL2025
Adversarial Attacks Against Automated Fact-Checking: A Survey
Fanzhen Liu, Alsharif Abuadbba, Kristen Moore +5
In an era where misinformation spreads freely, fact-checking (FC) plays a crucial role in verifying claims and promoting reliable information. While automated fact-checking (AFC) h…