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The distributed (federated) LLM is an important method for co-training the domain-specific LLM using siloed data.
“Trustzone: Integrated hardware and software security,”
Tiago Alves, · 2004
Earlier work this paper cites.
“Intel® software guard extensions (intel® sgx) support for dynamic memory management inside an enclave,”
Frank McKeen, Ilya Alexandrovich, Ittai Anati, Dror Caspi, Simon Johnson, Rebekah Leslie-Hurd, and Carlos Rozas, · 2016
Earlier work this paper cites.
“Amd memory encryption,”
David Kaplan, Jeremy Powell, and Tom Woller, · 2016
Earlier work this paper cites.
“Privacy-preserving deep learning via additively homomorphic encryption,”
Le Trieu Phong, Yoshinori Aono, Takuya Hayashi, Lihua Wang, and Shiho Moriai, · 2018
Earlier work this paper cites.
“Slalom: Fast, verifiable and private execution of neural networks in trusted hardware,”
Florian Tramer and Dan Boneh, · 2018
Earlier work this paper cites.
“cpsgd: Communication-efficient and differentially-private distributed sgd,”
Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, and Brendan McMahan, · 2018
Earlier work this paper cites.
“Beyond inferring class representatives: User-level privacy leakage from federated learning,”
Zhibo Wang, Mengkai Song, Zhifei Zhang, Yang Song, Qian Wang, and Hairong Qi, · 2019
Earlier work this paper cites.
“Securenn: 3-party secure computation for neural network training.,”
Sameer Wagh, Divya Gupta, and Nishanth Chandran, · 2019
Cited alongside, same era.
“P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks,”
Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Lam Tam, Zhengxiao Du, Zhilin Yang, and Jie Tang, · 2021
Cited alongside, same era.
“A survey on security and privacy of federated learning,”
Viraaji Mothukuri, Reza M Parizi, Seyedamin Pouriyeh, Yan Huang, Ali Dehghantanha, and Gautam Srivastava, · 2021
Cited alongside, same era.
“Model protection: Real-time privacy-preserving inference service for model privacy at the edge,”
Jiahui Hou, Huiqi Liu, Yunxin Liu, Yu Wang, Peng-Jun Wan, and Xiang-Yang Li, · 2021
Cited alongside, same era.
“Cblue: A chinese biomedical language understanding evaluation benchmark,”
Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, et al., · 2021
“LoRA: Low-rank adaptation of large language models,”
Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen, · 2022
Later among the works it cites.
“Model stealing attacks against inductive graph neural networks,”
Yun Shen, Xinlei He, Yufei Han, and Yang Zhang, · 2022
Later among the works it cites.
“Federatedscope-llm: A comprehensive package for fine-tuning large language models in federated learning,” 2023
Weirui Kuang, Bingchen Qian, Zitao Li, Daoyuan Chen, Dawei Gao, Xuchen Pan, Yuexiang Xie, Yaliang Li, Bolin Ding, and Jingren Zhou, · 2023
Later among the works it cites.
“Towards building the federated gpt: Federated instruction tuning,”
Jianyi Zhang, Saeed Vahidian, Martin Kuo, Chunyuan Li, Ruiyi Zhang, Guoyin Wang, and Yiran Chen, · 2023
Later among the works it cites.
“Promptcblue,” https://github.com/michael-wzhu/PromptCBLUE
2023
Later among the works it cites.
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Cited alongside, same era.
“Peft: State-of-the-art parameter-efficient fine-tuning methods,” https://github.com/huggingface/peft
Sourab Mangrulkar, Sylvain Gugger, Lysandre Debut, Younes Belkada, and Sayak Paul, · 2022
Cited alongside, same era.
“Intel. intel trust domain extensions,” https://www.intel.com/content/www/us/en/developer/tools/trust-domain-extensions/documentation.html
Cited in the paper.
Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, Weng Lam Tam, Zixuan Ma, Yufei Xue, Jidong Zhai, Wenguang Chen, Zhiyuan Liu, Peng Zhang, Yuxiao Dong, and Jie Tang, · 2023
Later among the works it cites.