Zobrazeno 1 - 10
of 189
pro vyhledávání: '"Qu, Youyang"'
Autor:
Qu, Youyang
Federated learning (FL) offers a compelling framework for training large language models (LLMs) while addressing data privacy and decentralization challenges. This paper surveys recent advancements in the federated learning of large language models,
Externí odkaz:
http://arxiv.org/abs/2406.09831
Autor:
Li, Conggai, Ni, Wei, Ding, Ming, Qu, Youyang, Chen, Jianjun, Smith, David, Zhang, Wenjie, Rakotoarivelo, Thierry
Many real-world interconnections among entities can be characterized as graphs. Collecting local graph information with balanced privacy and data utility has garnered notable interest recently. This paper delves into the problem of identifying and pr
Externí odkaz:
http://arxiv.org/abs/2405.11713
Autor:
Liu, Ming, Liu, Ran, Zhu, Ye, Wang, Hua, Qu, Youyang, Li, Rongsheng, Sheng, Yongpan, Buntine, Wray
ChatGPT has changed the AI community and an active research line is the performance evaluation of ChatGPT. A key challenge for the evaluation is that ChatGPT is still closed-source and traditional benchmark datasets may have been used by ChatGPT as t
Externí odkaz:
http://arxiv.org/abs/2405.00704
The worldwide adoption of machine learning (ML) and deep learning models, particularly in critical sectors, such as healthcare and finance, presents substantial challenges in maintaining individual privacy and fairness. These two elements are vital t
Externí odkaz:
http://arxiv.org/abs/2404.09391
Large Language Models (LLMs) are foundational to AI advancements, facilitating applications like predictive text generation. Nonetheless, they pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information from t
Externí odkaz:
http://arxiv.org/abs/2403.15779
Due to the greatly improved capabilities of devices, massive data, and increasing concern about data privacy, Federated Learning (FL) has been increasingly considered for applications to wireless communication networks (WCNs). Wireless FL (WFL) is a
Externí odkaz:
http://arxiv.org/abs/2312.08667
The popularization of intelligent healthcare devices and big data analytics significantly boosts the development of smart healthcare networks (SHNs). To enhance the precision of diagnosis, different participants in SHNs share health data that contain
Externí odkaz:
http://arxiv.org/abs/2306.16630
Machine Learning (ML) models have been shown to potentially leak sensitive information, thus raising privacy concerns in ML-driven applications. This inspired recent research on removing the influence of specific data samples from a trained ML model.
Externí odkaz:
http://arxiv.org/abs/2305.07512