most citedError Rate Bounds and Iterative Weighted Majority Voting for Crowdsourcing

60 citations · 60 across the 1 of their papers we have counts for

collaborators

5 papers

cs.CR20236 cited

A Comprehensive Overview of Backdoor Attacks in Large Language Models within Communication Networks

Haomiao Yang, Kunlan Xiang, Mengyu Ge +3

The Large Language Models (LLMs) are poised to offer efficient and intelligent services for future mobile communication networks, owing to their exceptional capabilities in languag…

cs.CR202335 cited

SigRec: Automatic Recovery of Function Signatures in Smart Contracts

Ting Chen, Zihao Li, Xiapu Luo +9

Millions of smart contracts have been deployed onto Ethereum for providing various services, whose functions can be invoked. For this purpose, the caller needs to know the function…

cs.CR20221 cited

Hercules: Boosting the Performance of Privacy-preserving Federated Learning

Guowen Xu, Xingshuo Han, Shengmin Xu +4

In this paper, we address the problem of privacy-preserving federated neural network training with users. We present Hercules, an efficient and high-precision training framewor…

cs.CR20222 cited

Privacy-preserving Decentralized Deep Learning with Multiparty Homomorphic Encryption

Guowen Xu, Guanlin Li, Shangwei Guo +2

Decentralized deep learning plays a key role in collaborative model training due to its attractive properties, including tolerating high network latency and less prone to single-po…

stat.ML201460 cited

Error Rate Bounds and Iterative Weighted Majority Voting for Crowdsourcing

Hongwei Li, Bin Yu

Crowdsourcing has become an effective and popular tool for human-powered computation to label large datasets. Since the workers can be unreliable, it is common in crowdsourcing to…