Xiaokai Zhou — accepted AI-conference papers

2026

Scheduling LLM Inference with Uncertainty-Aware Output Length Predictions

Haoyu Zheng, Yongqiang Zhang, Fangcheng Fu, Xiaokai Zhou, Hao Luo, Hongchao Zhu +4

ICML 2026poster

To schedule LLM inference, the \textit{shortest job first} (SJF) principle is favorable by prioritizing requests with short output lengths to avoid head-of-line (HOL) blocking. Existing methods usually predict a single output length for each request to facilitate scheduling. We argue that such a \te…

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2025

Model Rake: A Defense Against Stealing Attacks in Split Learning

Qinbo Zhang, Xiao Yan, Yanfeng Zhao, Fangcheng Fu, Quanqing Xu, Yukai Ding +3

IJCAI 2025

Split learning is a prominent framework for vertical federated learning, where multiple clients collaborate with a central server for model training by exchanging intermediate embeddings. Recently, it is shown that an adversarial server can exploit the intermediate embeddings to train surrogate mode

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