Hello, I am

Ke Xiao.

PhD Candidate at the University of Glasgow. I research Federated Learning, Edge Computing, and LLM Fine-tuning to build resilient, distributed AI systems.

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About Me

I am currently a PhD student in the KDES Group, School of Computing Science at the University of Glasgow.

I am dedicated to building robust distributed AI systems. Specifically, my work focuses on creating resilient Federated Learning (FL) frameworks that can withstand availability and security threats. I am also actively exploring distributed machine learning and the fine-tuning of Large Language Models. I have published my findings in leading journals and conferences such as IEEE TMC, IEEE T-IFS and IEEE ICDCS.

Research Interests

Federated Learning Edge Computing LLM Fine-tuning System Resilience Byzantine Fault Tolerance

Education

2024 - Present
PhD Candidate
University of Glasgow
2021 - 2024
Master of Computer Technology
Beijing Institute of Technology
2017 - 2021
Bachelor of Software Engineering
Dalian University of Technology

News

Recent updates & milestones
Dec 11, 2025 Publication

Paper accepted to IEEE T-IFS!

FLgym: Towards Robust and Byzantine-Resilient Federated Learning.

Jul 22, 2025 Award

Best Runner-Up Student Paper Award at IEEE ICDCS 2025!

Received for research on Byzantine-resilient FL.

Mar 27, 2025 Publication

Paper accepted to IEEE ICDCS 2025!

Unified Parallel Semantic Log Parsing based on Causal Graph Construction for Attack Attribution.

Mar 27, 2025 Publication

Paper accepted to IEEE ICDCS 2025!

A Robust Byzantine-Resilient Framework for Federated Learning.

Mar 27, 2025 Publication

Paper accepted to IEEE ICDCS 2025!

Resilient Inference for Personalized Federated Learning in Edge Environments.

Oct 01, 2024 Career

Joined University of Glasgow!

Joined the KDES family in the School of Computing.

Selected Publications

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