About

Short Bio

I am currently a Ph.D. student advised by Prof. Jing Gao in the Elmore Family School of Electrical and Computer Engineering at Purdue University. Before joining Purdue in 2022, I worked with Prof. Song Guo at the Pervasive Edge Intelligence Laboratory (PEILab). I obtained my B.Sc. degree in Computing (major) and Applied Mathematics (minor) from The Hong Kong Polytechnic University in 2020. During my Ph.D. study, I have been working as a research intern at Alibaba Group. I have served as a reviewer for a number of prestigious conferences, such as ICML, NeurIPS, ICLR, and KDD. I was a recipient of Best Student Paper from ACM BSCI'19.

Research Interest

Federated Learning

Large-scale Optimization

Large Language Model

LLM-based Multi-agents

News

  • [Jan. 2025] Our paper on federated RLHF is accepted by ICLR'25.
  • [Oct. 2024] I receive a travel grant from SenSys'24.
  • [Sept. 2024] Our paper on model-heterogeneous federated learning is accepted by NeurIPS'24.
  • [Sept. 2024] Our paper on LLM copyright protection is accepted by EMNLP'24.
  • [Sept. 2024] Our paper on heterogeneous multi-modal federated learning is accepted by SenSys'24.
Education

Education

August 2022 - Present
Purdue University
West Lafayette, Indiana, United States
September 2020 - August 2022
The Hong Kong Polytechnic University
Hong Kong
  • Thesis: Gradient-wise Optimization for Distributed Machine Learning [PDF]
August 2016 - September 2020
The Hong Kong Polytechnic University
Hong Kong
  • Minor in Applied Mathematics
February 2019 - July 2019
The University of Twente
Enschede, Overijssel, Netherlands
Experience

Work Experience

Purdue University West Lafayette, Indiana, United States

Graduate Research Assistant (Advisor: Prof. Jing Gao)
August 2022 - Present

Alibaba Group Bellevue, Washington, United States

Research Intern (Mentor: Dr. Yaliang Li and Dr. Zitao Li)
May 2023 - August 2023

Pervasive Edge Intelligence Laboratory (PEILab), The Hong Kong Polytechnic University Hong Kong

Research Assistant (Advisor: Prof. Song Guo)
September 2020 - August 2022

The Chinese University of Hong Kong (Shenzhen) Shenzhen, Guangdong, China

Research Assistant (Advisor: Prof. Wei Cai)
July 2019 - September 2019

The University of British Columbia Vancouver, British Columbia, Canada

Research Assistant (Advisor: Prof. Victor C.M. Leung)
July 2018 - September 2018
Teaching

Teaching

ENG 2003 Information Technology

Teaching Assistant @ The Hong Kong Polytechnic University
Spring 2022

COMP 1011 Programming Fundamentals

Teaching Assistant @ The Hong Kong Polytechnic University
Fall 2021

Services

Professional Activities

Conference TPC/Reviewer
ACM BSCI 2020, ECML-PKDD 2020, ICML 2022, NeurIPS 2022, ICML 2023, NeurIPS 2023, ICLR 2024, ICML 2024, KDD 2024, NeurIPS 2024, ACL Rolling Review (June 2024), KDD 2025, ICLR 2025
Journal Reviewer
IEEE OJ-CS 2021, ACM TOMM 2022
Publications

Conference Paper

[1]Feijie Wu, Xiaoze Liu, Haoyu Wang, Xingchen Wang, Lu Su, Jing Gao, "Towards Federated RLHF with Aggregated Client Preference for LLMs," in Proc. of the International Conference on Learning Representations (ICLR'25), 2025. [PDF] (acceptance rate: 32.08%)
[2]Feijie Wu, Xingchen Wang, Yaqing Wang, Tianci Liu, Lu Su, Jing Gao, "FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction," in Proc. of Advances in Neural Information Processing Systems (NeurIPS'24), 2024. [PDF] [Code] (acceptance rate: 25.8%)
[3]Xiaoze Liu, Ting Sun, Tianyang Xu, Feijie Wu, Cunxiang Wang, Xiaoqian Wang, Jing Gao, "SHIELD: Evaluation and Defense Strategies for Copyright Compliance in LLM Text Generation," in Proc. of the Conference on Empirical Methods in Natural Language Processing (EMNLP'24), 2024. [PDF] [Code]
[4]Xingchen Wang, Haoyu Wang, Feijie Wu, Tianci Liu, Qiming Cao, Lu Su, "Towards Efficient Heterogeneous Multi-Modal Federated Learning," in Proc. of the ACM Conference on Embedded Networked Sensor Systems (Sensys'24), 2024. [PDF] (acceptance rate: 58/313=18.5%)
[5]Feijie Wu, Zitao Li, Yaliang Li, Bolin Ding, Jing Gao, "FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model," in Proc. of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'24), 2024. [PDF] [Code] (acceptance rate: 20% (research track))
[6]Tianci Liu, Haoyu Wang, Feijie Wu, Hengtong Zhang, Pan Li, Lu Su, Jing Gao, "Towards Poisoning Fair Representations," in Proc. of the International Conference on Learning Representations (ICLR'24), 2024. [PDF] (acceptance rate: 31%)
[7]Feijie Wu, Song Guo, Zhihao Qu, Shiqi He, Ziming Liu, Jing Gao, "Anchor Sampling for Federated Learning with Partial Client Participation," in Proc. of the International Conference on Machine Learning (ICML'23), 2023. [PDF] [Code] (acceptance rate: 1827/6538=28%)
[8]Shiqi He, Qifan Yan, Feijie Wu, Lanjun Wang, Mathias Lécuyer, Ivan Beschastnikh, "GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning," in Proc. of the Annual Conference on Machine Learning and Systems (MLSys'23), 2023. [PDF] [Code] (acceptance rate: 22%)
[9]Haoyu Wang, Yaqing Wang, Feijie Wu, Hongfei Xue, Jing Gao, "Macular: a Multi-Task Adversarial Framework for Cross-Lingual Natural Language Understanding," in Proc. of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'23), 2023. [PDF] (acceptance rate: 184/725=25% (ADS track))
[10]Feijie Wu*, Shiqi He*, Song Guo, Zhihao Qu, Haozhao Wang, Weihua Zhuang, Jie Zhang, "Sign Bit is Enough: A Learning Synchronization Framework for Multi-hop All-reduce with Ultimate Compression," in Proc. of the ACM/IEEE Design Automation Conference (DAC'22), 2022. [PDF] (acceptance rate: 23%)
[11]Jie Zhang, Song Guo, Xiaosong Ma, Haozhao Wang, Wenchao Xu, Feijie Wu, "Parameterized Knowledge Transfer for Personalized Federated Learning," in Proc. of Advances in Neural Information Processing Systems (NeurIPS'21), 2021. [PDF] (acceptance rate: 2344/9122=26%)
[12]Feijie Wu, Ho Yin Yuen, Henry C.B. Chan, Victor C.M. Leung, Wei Cai, "Infinity Battle: A Glance at How Blockchain Techniques Serve in a Serverless Gaming System," in Proc. of the ACM International Conference on Multimedia (MM'20), 2020. [PDF] [Code]
[13]Ho Yin Yuen, Feijie Wu, Wei Cai, Henry C.B. Chan, Qiao Yan, Victor C.M. Leung, "Proof-of-Play: A Novel Consensus Model for Blockchain-Based Peer-to-Peer Gaming System," in Proc. of the ACM International Symposium on Blockchain and Secure Critical Infrastructure (BSCI'19), 2019. [PDF] (acceptance rate: 12/44=27%)
Best Student Paper Award

Journal Paper

[14] Tao Guo, Song Guo, Feijie Wu, Wenchao Xu, Jiewei Zhang, Qihua Zhou, Quan Chen, Weihua Zhuang, "Tree Learning: Towards Promoting Coordination in Scalable Multi-Client Training Acceleration," IEEE Transactions on Mobile Computing, vol. 23, no. 3, 2024. [PDF]
[15]Xiaoze Liu, Feijie Wu, Tianyang Xu, Zhuo Chen, Yichi Zhang, Xiaoqian Wang, Jing Gao, "Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs," IEEE Data Engineering Bulletin, vol. 47, no. 4, 2024. [PDF] [Code]
[16]Feijie Wu, Song Guo, Haozhao Wang, Haobo Zhang, Zhihao Qu, Jie Zhang, Ziming Liu, "From Deterioration to Acceleration: A Calibration Approach to Rehabilitating Step Asynchronism in Federated Optimization," IEEE Transactions on Parallel and Distributed Systems, vol. 34, no. 5, 2023. [PDF]
[17]Feijie Wu, Ho Yin Yuen, Henry C. B. Chan, Victor C. M. Leung, Wei Cai, "Facilitating Serverless Match-Based Online Games with Novel Blockchain Technologies," ACM Transactions on Internet Technology, vol. 23, no. 1, 2023. [PDF]

Preprint Paper

[18]Feijie Wu, Zitao Li, Fei Wei, Yaliang Li, Bolin Ding, Jing Gao, "Talk to Right Specialists: Routing and Planning in Multi-agent System for Question Answering," 2025. [PDF]
[19] Feijie Wu, Shiqi He, Yutong Yang, Haozhao Wang, Zhihao Qu, Song Guo, Weihua Zhuang, "On the Convergence of Quantized Parallel Restarted SGD for Central Server Free Distributed Training," 2020. [PDF]

* indicates equal contribution.

* indicates equal contribution.