4 papers
PRODIGY: Enabling In-context Learning Over Graphs
Qian Huang, Hongyu Ren, Peng Chen +4
In-context learning is the ability of a pretrained model to adapt to novel and diverse downstream tasks by conditioning on prompt examples, without optimizing any parameters. While…
Universal Adversarial Backdoor Attacks to Fool Vertical Federated Learning in Cloud-Edge Collaboration
Peng Chen, Xin Du, Zhihui Lu +1
Vertical federated learning (VFL) is a cloud-edge collaboration paradigm that enables edge nodes, comprising resource-constrained Internet of Things (IoT) devices, to cooperatively…
XFL: A High Performace, Lightweighted Federated Learning Framework
Hong Wang, Yuanzhi Zhou, Chi Zhang +4
This paper introduces XFL, an industrial-grade federated learning project. XFL supports training AI models collaboratively on multiple devices, while utilizes homomorphic encryptio…
Explaining with Greater Support: Weighted Column Sampling Optimization for q-Consistent Summary-Explanations
Chen Peng, Zhengqi Dai, Guangping Xia +2
Machine learning systems have been extensively used as auxiliary tools in domains that require critical decision-making, such as healthcare and criminal justice. The explainability…