NLP有着丰富的移动应用,为了支持这些应用,一个基础NLP模型,需要在联邦学习环境下进行微调。 这个过程需要成千上万的有标签数据支撑。然而,移动用户经常缺乏意愿和专业知识去标记他们的数据,这种只有少量训练数据具有标签的场景在本文中被称作Few-Shot scenario (FedFSL),现已成为移动NLP应用的瓶颈。 2.动机 针对Few-...
[ACL2023]FEDLEGAL: The First Real-World Federated Learning Benchmark for Legal NLP 用分散的数据提升大模型的性能 利用联邦学习的分布式特性,有机会有效利用分散的小规模数据来改进大模型。虽然大模型通常是在大规模集中数据集上进行训练的,但它们可能存在领域差距,对真实数据分布的覆盖存在限制。联邦学习可以通过...
2022-04-11Few-Shot Object Detection in Unseen Domains~~ 2022-04-08BioBART: Pretraining and Evaluation of A Biomedical Generative Language ModelAccepted by BioNLP 2022, Long Papercode 2022-04-07Pin the Memory: Learning to Generalize Semantic SegmentationAccepted to CVPR 2022~ ...
To the best of our knowledge, this may be the first time that FL is being utilized in the context of a RS application. Analysis of classification results yielded by the highest performing model (ViT-Base) by considering multiple scenarios of dropped clients. In particular, we focused our ...
Machine learning (ML) has succeeded in improving our daily routines by enabling automation and improved decision making in a variety of industries such as healthcare, finance, and transportation, resulting in increased efficiency and production. However, the development and widespread use of this techn...
Machine learning (ML) has succeeded in improving our daily routines by enabling automation and improved decision making in a variety of industries such as healthcare, finance, and transportation, resulting in increased efficiency and production. However, the development and widespread use of this techn...