most citedLIPS: A Light Intensity Based Positioning System For Indoor Environments

9 citations · 9 across the 1 of their papers we have counts for

collaborators

5 papers

cs.CR20242 cited

Machine Unlearning in Large Language Models

Kongyang Chen, Zixin Wang, Bing Mi +4

Recently, large language models (LLMs) have emerged as a notable field, attracting significant attention for its ability to automatically generate intelligent contents for various…

cs.CR20243 cited

EdgeLeakage: Membership Information Leakage in Distributed Edge Intelligence Systems

Kongyang Chen, Yi Lin, Hui Luo +4

In contemporary edge computing systems, decentralized edge nodes aggregate unprocessed data and facilitate data analytics to uphold low transmission latency and real-time data proc…

cs.CR2024

Membership Information Leakage in Federated Contrastive Learning

Kongyang Chen, Wenfeng Wang, Zixin Wang +3

Federated Contrastive Learning (FCL) represents a burgeoning approach for learning from decentralized unlabeled data while upholding data privacy. In FCL, participant clients colla…

cs.CR20231 cited

BAGEL: Backdoor Attacks against Federated Contrastive Learning

Yao Huang, Kongyang Chen, Jiannong Cao +5

Federated Contrastive Learning (FCL) is an emerging privacy-preserving paradigm in distributed learning for unlabeled data. In FCL, distributed parties collaboratively learn a glob…

cs.NI20149 cited

LIPS: A Light Intensity Based Positioning System For Indoor Environments

Bo Xie, Guang Tan, Yunhuai Liu +3

This paper presents LIPS, a Light Intensity based Positioning System for indoor environments. The system uses off-the-shelf LED lamps as signal sources, and uses light sensors as s…