Shenghui Li

Postdoctoral position at Department of Electrical Engineering; Networked Embedded Systems

E-mail:: shenghui.li@angstrom.uu.se
Visiting address:: Ångström, Lägerhyddsvägen 1
752 37 Uppsala
Postal address:: Box 65
751 03 Uppsala

Download contact information for Shenghui Li at Department of Electrical Engineering; Networked Embedded Systems

PhD student at Department of Information Technology; Division of Computer Systems

E-mail:: shenghui.li@it.uu.se
Visiting address:: Hus 10, Regementsvägen 10
Postal address:: Box 337
751 05 UPPSALA

Download contact information for Shenghui Li at Department of Information Technology; Division of Computer Systems

ORCID:: 0000-0002-2687-2697

Research

My research web page

Publications

Recent publications

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

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

Part of IEEE Transactions on Big Data, p. 975-988, 2024
- DOI for An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning
Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning

Li, Shenghui; Ngai, Edith C. H.; Ye, Fanghua; Ju, Li et al.

Part of 2024 IEEE/ACM Ninth International Conference on Internet-of-Things Design and Implementation (IoTDI), p. 158-169, 2024
- DOI for Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning
Robust Federated Learning: Defending Against Byzantine and Jailbreak Attacks

Li, Shenghui

2024
- Download full text (pdf) of Robust Federated Learning: Defending Against Byzantine and Jailbreak Attacks
Byzantine-Robust Aggregation in Federated Learning Empowered Industrial IoT

Li, Shenghui; Ngai, Edith; Voigt, Thiemo

Part of IEEE Transactions on Industrial Informatics, p. 1165-1175, 2023
- DOI for Byzantine-Robust Aggregation in Federated Learning Empowered Industrial IoT
Auto-weighted Robust Federated Learning with Corrupted Data Sources

Li, Shenghui; Ngai, Edith; Ye, Fanghua; Voigt, Thiemo

Part of ACM Transactions on Intelligent Systems and Technology, p. 1-20, 2022
- DOI for Auto-weighted Robust Federated Learning with Corrupted Data Sources

All publications

Articles in journal

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

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

Part of IEEE Transactions on Big Data, p. 975-988, 2024
- DOI for An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning
Byzantine-Robust Aggregation in Federated Learning Empowered Industrial IoT

Li, Shenghui; Ngai, Edith; Voigt, Thiemo

Part of IEEE Transactions on Industrial Informatics, p. 1165-1175, 2023
- DOI for Byzantine-Robust Aggregation in Federated Learning Empowered Industrial IoT
Auto-weighted Robust Federated Learning with Corrupted Data Sources

Li, Shenghui; Ngai, Edith; Ye, Fanghua; Voigt, Thiemo

Part of ACM Transactions on Intelligent Systems and Technology, p. 1-20, 2022
- DOI for Auto-weighted Robust Federated Learning with Corrupted Data Sources

Comprehensive doctoral thesis

Robust Federated Learning: Defending Against Byzantine and Jailbreak Attacks

Li, Shenghui

2024
- Download full text (pdf) of Robust Federated Learning: Defending Against Byzantine and Jailbreak Attacks

Conference papers

Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning

Li, Shenghui; Ngai, Edith C. H.; Ye, Fanghua; Ju, Li et al.

Part of 2024 IEEE/ACM Ninth International Conference on Internet-of-Things Design and Implementation (IoTDI), p. 158-169, 2024
- DOI for Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning
Slot Self-Attentive Dialogue State Tracking

Ye, Fanghua; Manotumruksa, Jarana; Zhang, Qiang; Li, Shenghui et al.

Part of Proceedings of the World Wide Web Conference 2021 (WWW 2021), p. 1598-1608, 2021
- DOI for Slot Self-Attentive Dialogue State Tracking
- Download full text (pdf) of Slot Self-Attentive Dialogue State Tracking

Manuscripts (preprints)

Synergizing Foundation Models And Federated Learning: A Survey

Li, Shenghui; Ye, Fanghua; Fang, Meng; Zhao, Jiaxu et al.
PEFT-as-an-Attack! Jailbreaking Language Models during Federated Parameter-Efficient Fine-Tuning

Li, Shenghui; Ngai, Edith; Ye, Fanghua; Voigt, Thiemo