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Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage
This approach manipulates a pre-trained language model to increase the leakage of private data during the fine-tuning process.
Second-Order Information Matters: Revisiting Machine Unlearning for Large Language Models
With the rapid development of Large Language Models (LLMs), we have witnessed intense competition among the major LLM products like ChatGPT, LLaMa, and Gemini.
FLTrojan: Privacy Leakage Attacks against Federated Language Models Through Selective Weight Tampering
Federated learning (FL) has become a key component in various language modeling applications such as machine translation, next-word prediction, and medical record analysis.
FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks
This highlights the need to design FL systems that are secure and robust against malicious participants' actions while also ensuring high utility, privacy of local data, and efficiency.
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