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pro vyhledávání: '"Du, Yaxin"'
By leveraging massively distributed data, federated learning (FL) enables collaborative instruction tuning of large language models (LLMs) in a privacy-preserving way. While FL effectively expands the data quantity, the issue of data quality remains
Externí odkaz:
http://arxiv.org/abs/2410.11540
Federated Domain-specific Instruction Tuning (FedDIT) utilizes limited cross-client private data together with server-side public data for instruction augmentation, ultimately boosting model performance within specific domains. To date, the factors a
Externí odkaz:
http://arxiv.org/abs/2409.20135
Autor:
Ye, Rui, Ge, Rui, Zhu, Xinyu, Chai, Jingyi, Du, Yaxin, Liu, Yang, Wang, Yanfeng, Chen, Siheng
Federated learning has enabled multiple parties to collaboratively train large language models without directly sharing their data (FedLLM). Following this training paradigm, the community has put massive efforts from diverse aspects including framew
Externí odkaz:
http://arxiv.org/abs/2406.04845
In the current landscape of foundation model training, there is a significant reliance on public domain data, which is nearing exhaustion according to recent research. To further scale up, it is crucial to incorporate collaboration among multiple spe
Externí odkaz:
http://arxiv.org/abs/2403.04529
Autor:
Ye, Rui, Wang, Wenhao, Chai, Jingyi, Li, Dihan, Li, Zexi, Xu, Yinda, Du, Yaxin, Wang, Yanfeng, Chen, Siheng
Trained on massive publicly available data, large language models (LLMs) have demonstrated tremendous success across various fields. While more data contributes to better performance, a disconcerting reality is that high-quality public data will be e
Externí odkaz:
http://arxiv.org/abs/2402.06954
In federated learning (FL), data heterogeneity is one key bottleneck that causes model divergence and limits performance. Addressing this, existing methods often regard data heterogeneity as an inherent property and propose to mitigate its adverse ef
Externí odkaz:
http://arxiv.org/abs/2312.05966
Publikováno v:
In Chemical Engineering Journal 1 October 2024 497
Akademický článek
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Akademický článek
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Autor:
Zhao, Yi, Peng, Yao, Yang, Zhongzhen, Lu, Jiaqi, Li, Ru, Shi, Yuesen, Du, Yaxin, Zhao, Ze, Hai, Li, Wu, Yong
Publikováno v:
In European Journal of Medicinal Chemistry 5 May 2022 235