[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...
316 FedJudge: Federated Legal Large Language Model Linan Yue, Qi Liu, Yichao Du, Weibo Gao, Ye Liu, Fangzhou Yao 2023-09-15 arXiv https://github.com/yuelinan/FedJudge http://arxiv.org/abs/2309.08173v3 317 FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental Regularization...
PO6: The engineer and society Societal Issues, Health and Safety, Legal Considerations, Cultural Issues, Professional Responsibilities, Communication PO7: Environmental and sustainability Societal Impact, Environmental Impact, Sustainable Development, Ethical Considerations, Regulatory Compliance, Communication and...
235 FedJudge: Federated Legal Large Language Model Linan Yue, Qi Liu, Yichao Du, Weibo Gao, Ye Liu, Fangzhou Yao 2023-09-15 arXiv https://github.com/yuelinan/FedJudge http://arxiv.org/abs/2309.08173v3 236 FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental Regularization...
Large Language Models (LLM) and foundation models are popular as they offer new opportunities for individuals and businesses to improve natural language processing, interact with data, and retrieve information faster. However, training or fine-tuning LLMs requires a vast amount of data, which can ...
摘要To protect user privacy and meet legal regulations, federated learning (FL) is attracting significant attention. Training neural machine translation (NMT) models with traditional FL algorithm (e.g., FedAvg) typically relies on multi-round model-based interactions. However, it is impractical and ...
3 Model performance on COVID-positive and COVID-negative patients. ROCs of the best global model in comparison to the mean ROCs of models trained on local datasets to predict 24/72-h oxygen treatment devices for COVID positive/negative patients respectively, using the test data of 5 large ...
Federated learning (Bugshan et al., 2023) is fundamentally a distributed machine learning technique or framework that aspires to achieve collaborative modeling, enhancing model performance while upholding data privacy and legal compliance. However, under the privacy constraint DG ReID scenario, a notable...
FEDLEGAL: The First Real-World Federated Learning Benchmark for Legal NLP [pdf] [code] Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms [pdf] [code] Client-Customized Adaptation for Parameter-Efficient Federated Learning [pdf] [code] Communication Efficient ...
(3) According to GDPR, the consent of the individuals or their legal representatives must be obtained before grades are processed. Students with poorer performance may withhold consent due to privacy concerns, introducing selection bias and limiting CSDID’s utility with central learning (Figure 1A)...