most citedAn Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning

90 citations · 93 across the 2 of their papers we have counts for

collaborators

7 papers

cs.IR20252 cited

A Survey on Multimodal Recommender Systems: Recent Advances and Future Directions

Jinfeng Xu, Zheyu Chen, Shuo Yang +5

Acquiring valuable data from the rapidly expanding information on the internet has become a significant concern, and recommender systems have emerged as a widely used and effective…

cs.CR20241 cited

PEFT-as-an-Attack! Jailbreaking Language Models during Federated Parameter-Efficient Fine-Tuning

Shenghui Li, Edith C. -H. Ngai, Fanghua Ye +1

Federated Parameter-Efficient Fine-Tuning (FedPEFT) has emerged as a promising paradigm for privacy-preserving and efficient adaptation of Pre-trained Language Models (PLMs) in Fed…

cs.CV2024

Beyond Finite Data: Towards Data-free Out-of-distribution Generalization via Extrapolation

Yijiang Li, Sucheng Ren, Weipeng Deng +4

Out-of-distribution (OOD) generalization is a favorable yet challenging property for deep neural networks. The core challenges lie in the limited availability of source domains tha…

cs.CR20242 cited

AOC-IDS: Autonomous Online Framework with Contrastive Learning for Intrusion Detection

Xinchen Zhang, Running Zhao, Zhihan Jiang +4

The rapid expansion of the Internet of Things (IoT) has raised increasing concern about targeted cyber attacks. Previous research primarily focused on static Intrusion Detection Sy…

cs.SD202321 cited

Radio2Text: Streaming Speech Recognition Using mmWave Radio Signals

Running Zhao, Jiangtao Yu, Hang Zhao +1

Millimeter wave (mmWave) based speech recognition provides more possibility for audio-related applications, such as conference speech transcription and eavesdropping. However, cons…

cs.LG202390 cited

An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning

Shenghui Li, Edith C. -H. Ngai, Thiemo Voigt

Byzantine-robust federated learning aims at mitigating Byzantine failures during the federated training process, where malicious participants may upload arbitrary local updates to…