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FedCAP: Robust Federated Learning via Customized Aggregation and Personalization

Autor: Li, Youpeng, Wang, Xinda, Yu, Fuxun, Sun, Lichao, Zhang, Wenbin, Wang, Xuyu

Federated learning (FL), an emerging distributed machine learning paradigm, has been applied to various privacy-preserving scenarios. However, due to its distributed nature, FL faces two key issues: the non-independent and identical distribution (non

Externí odkaz: http://arxiv.org/abs/2410.13083

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On the Efficiency of Privacy Attacks in Federated Learning

Autor: Tabassum, Nawrin, Chow, Ka-Ho, Wang, Xuyu, Zhang, Wenbin, Wu, Yanzhao

Recent studies have revealed severe privacy risks in federated learning, represented by Gradient Leakage Attacks. However, existing studies mainly aim at increasing the privacy attack success rate and overlook the high computation costs for recoverin

Externí odkaz: http://arxiv.org/abs/2404.09430

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TrustLLM: Trustworthiness in Large Language Models

Autor: Huang, Yue, Sun, Lichao, Wang, Haoran, Wu, Siyuan, Zhang, Qihui, Li, Yuan, Gao, Chujie, Huang, Yixin, Lyu, Wenhan, Zhang, Yixuan, Li, Xiner, Liu, Zhengliang, Liu, Yixin, Wang, Yijue, Zhang, Zhikun, Vidgen, Bertie, Kailkhura, Bhavya, Xiong, Caiming, Xiao, Chaowei, Li, Chunyuan, Xing, Eric, Huang, Furong, Liu, Hao, Ji, Heng, Wang, Hongyi, Zhang, Huan, Yao, Huaxiu, Kellis, Manolis, Zitnik, Marinka, Jiang, Meng, Bansal, Mohit, Zou, James, Pei, Jian, Liu, Jian, Gao, Jianfeng, Han, Jiawei, Zhao, Jieyu, Tang, Jiliang, Wang, Jindong, Vanschoren, Joaquin, Mitchell, John, Shu, Kai, Xu, Kaidi, Chang, Kai-Wei, He, Lifang, Huang, Lifu, Backes, Michael, Gong, Neil Zhenqiang, Yu, Philip S., Chen, Pin-Yu, Gu, Quanquan, Xu, Ran, Ying, Rex, Ji, Shuiwang, Jana, Suman, Chen, Tianlong, Liu, Tianming, Zhou, Tianyi, Wang, William, Li, Xiang, Zhang, Xiangliang, Wang, Xiao, Xie, Xing, Chen, Xun, Wang, Xuyu, Liu, Yan, Ye, Yanfang, Cao, Yinzhi, Chen, Yong, Zhao, Yue

Large language models (LLMs), exemplified by ChatGPT, have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs present many challenges, particularly in the realm of trustworthiness. Ther

Externí odkaz: http://arxiv.org/abs/2401.05561

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Rethinking Learning Rate Tuning in the Era of Large Language Models

Autor: Jin, Hongpeng, Wei, Wenqi, Wang, Xuyu, Zhang, Wenbin, Wu, Yanzhao

Large Language Models (LLMs) represent the recent success of deep learning in achieving remarkable human-like predictive performance. It has become a mainstream strategy to leverage fine-tuning to adapt LLMs for various real-world applications due to

Externí odkaz: http://arxiv.org/abs/2309.08859

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