research
∙
07/12/2023
Instruction Mining: High-Quality Instruction Data Selection for Large Language Models
Large language models typically undergo two training stages, pretraining...
share
research
∙
06/08/2023
FedMLSecurity: A Benchmark for Attacks and Defenses in Federated Learning and LLMs
This paper introduces FedMLSecurity, a benchmark that simulates adversar...
research
∙
05/22/2023
Watermarking Text Data on Large Language Models for Dataset Copyright Protection
Large Language Models (LLMs), such as BERT and GPT-based models like Cha...
research
∙
02/18/2023
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT
The Pretrained Foundation Models (PFMs) are regarded as the foundation f...
research
∙
10/18/2022
Transferable Unlearnable Examples
With more people publishing their personal data online, unauthorized dat...
research
∙
05/25/2022
End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models
We propose the end-to-end multimodal fact-checking and explanation gener...
research
∙
03/17/2022
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation
Federated Learning (FL) on knowledge graphs (KGs) has yet to be as well ...
research
∙
11/29/2021
FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization
The underlying assumption of recent federated learning (FL) paradigms is...
research
∙
06/30/2021
Global Knowledge Distillation in Federated Learning
Knowledge distillation has caught a lot of attention in Federated Learni...
research
∙
04/16/2021
Membership Inference Attacks on Knowledge Graphs
Knowledge graphs have become increasingly popular supplemental informati...
research
∙
09/11/2020
Federated Model Distillation with Noise-Free Differential Privacy
Conventional federated learning directly averaging model weights is only...
research
∙
08/02/2020
