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Xiaolei Liu

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8 papers

2 author rows

AAAI Conference 2026 Conference Paper

DoBlock: Blocking Malicious Association Propagation for Backdoor-Robust Federated Learning Under Domain Skew

Zhou Tan
De Li
Yirui Huang
Duanshu Fang
Jia-Li Yin
Xiaolei Liu
Songze Li
Shouling Ji

Federated Learning (FL) enables privacy-preserving distributed training but remains vulnerable to backdoor attacks. Attackers can embed malicious trigger-label associations into the global model by participating in the aggregation process. Existing defense methods typically defend against backdoor attacks by detecting and filtering malicious updates that deviate from benign ones. However, we find that these defenses fail under domain skew, where differing feature distributions across clients increase update heterogeneity, making it harder to distinguish malicious updates from benign ones. To address this challenge, we propose DoBlock, a novel defense that utilizes an aggregatable domain infuser incapable of embedding malicious associations, through federated training to facilitate cross-domain knowledge sharing. Moreover, DoBlock prevents malicious association propagation by isolating local models from aggregation, as local models remain client-specific and rely solely on local data for training. Experiments on five domain skew datasets (Digits, PACS, VLCS, Office-Caltech10, and DomainNet) show that DoBlock maintains attack success rates below 2.5%, while achieving the highest main task accuracy, demonstrating superior robustness without sacrificing benign performance.

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AAAI Conference 2026 Conference Paper

LoopLLM: Transferable Energy-Latency Attacks in LLMs via Repetitive Generation

Xingyu Li
Xiaolei Liu
Cheng Liu
Yixiao Xu
Kangyi Ding
Bangzhou Xin
Jia-Li Yin

As large language models (LLMs) scale, their inference incurs substantial computational resources, exposing them to energy-latency attacks, where crafted prompts induce high energy and latency cost. Existing attack methods aim to prolong output by delaying the generation of termination symbols. However, as the output grows longer, controlling the termination symbols through input becomes difficult, making these methods less effective. Therefore, we propose LoopLLM, an energy-latency attack framework based on the observation that repetitive generation can trigger low-entropy decoding loops, reliably compelling LLMs to generate until their output limits. LoopLLM introduces (1) a repetition-inducing prompt optimization that exploits autoregressive vulnerabilities to induce repetitive generation, and (2) a token-aligned ensemble optimization that aggregates gradients to improve cross-model transferability. Extensive experiments on 12 open-source and 2 commercial LLMs show that LoopLLM significantly outperforms existing methods, achieving over 90% of the maximum output length, compared to 20% for baselines, and improving transferability by around 40% to DeepSeek-V3 and Gemini 2.5 Flash.

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UAI Conference 2025 Conference Paper

i 2 VAE: Interest Information Augmentation with Variational Regularizers for Cross-Domain Sequential Recommendation

Xuying Ning
Wujiang Xu
Tianxin Wei
Xiaolei Liu

Cross-Domain Sequential Recommendation (CDSR) leverages user behaviors across multiple domains to mitigate data sparsity and cold-start challenges in Single-Domain Sequential Recommendation. Existing methods primarily rely on shared users (overlapping users) to learn transferable interest representations. However, these approaches have limited information propagation, benefiting mainly overlapping users and those with rich interaction histories while neglecting non-overlapping (cold-start) and long-tailed users, who constitute the majority in real-world scenarios. To address this issue, we propose i$^2$VAE, a novel variational autoencoder (VAE)-based framework that enhances user interest learning with mutual information-based regularizers. i$^2$VAE improves recommendations for cold-start and long-tailed users while maintaining strong performance across all user groups. Specifically, cross-domain and disentangling regularizers extract transferable features for cold-start users, while a pseudo-sequence generator synthesizes interactions for long-tailed users, refined by a denoising regularizer to filter noise and preserve meaningful interest signals. Extensive experiments demonstrate that i$^2$VAE outperforms state-of-the-art methods, underscoring its effectiveness in real-world CDSR applications. Code and datasets are available at https: //github. com/WujiangXu/IM-VAE.

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NeurIPS Conference 2024 Conference Paper

A Layer-Wise Natural Gradient Optimizer for Training Deep Neural Networks

Xiaolei Liu
Shaoshuai Li
Kaixin Gao
Binfeng Wang

Second-order optimization algorithms, such as the Newton method and the natural gradient descent (NGD) method exhibit excellent convergence properties for training deep neural networks, but the high computational cost limits its practical application. In this paper, we focus on the NGD method and propose a novel layer-wise natural gradient descent (LNGD) method to further reduce computational costs and accelerate the training process. Specifically, based on the block diagonal approximation of the Fisher information matrix, we first propose the layer-wise sample method to compute each block matrix without performing a complete back-propagation. Then, each block matrix is approximated as a Kronecker product of two smaller matrices, one of which is a diagonal matrix, while keeping the traces equal before and after approximation. By these two steps, we provide a new approximation for the Fisher information matrix, which can effectively reduce the computational cost while preserving the main information of each block matrix. Moreover, we propose a new adaptive layer-wise learning rate to further accelerate training. Based on these new approaches, we propose the LNGD optimizer. The global convergence analysis of LNGD is established under some assumptions. Experiments on image classification and machine translation tasks show that our method is quite competitive compared to the state-of-the-art methods.

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IJCAI Conference 2024 Conference Paper

An Embarrassingly Simple Approach to Enhance Transformer Performance in Genomic Selection for Crop Breeding

Renqi Chen
Wenwei Han
Haohao Zhang
Haoyang Su
Zhefan Wang
Xiaolei Liu
Hao Jiang
Wanli Ouyang

Genomic selection (GS), as a critical crop breeding strategy, plays a key role in enhancing food production and addressing the global hunger crisis. The predominant approaches in GS currently revolve around employing statistical methods for prediction. However, statistical methods often come with two main limitations: strong statistical priors and linear assumptions. A recent trend is to capture the non-linear relationships between markers by deep learning. However, as crop datasets are commonly long sequences with limited samples, the robustness of deep learning models, especially Transformers, remains a challenge. In this work, to unleash the unexplored potential of attention mechanism for the task of interest, we propose a simple yet effective Transformer-based framework that enables end-to-end training of the whole sequence. Via experiments on rice3k and wheat3k datasets, we show that, with simple tricks such as k-mer tokenization and random masking, Transformer can achieve overall superior performance against seminal methods on GS tasks of interest.

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AAAI Conference 2021 Conference Paper

A Trace-restricted Kronecker-Factored Approximation to Natural Gradient

Kaixin Gao
Xiaolei Liu
Zhenghai Huang
Min Wang
Zidong Wang
Dachuan Xu
Fan Yu

Second-order optimization methods have the ability to accelerate convergence by modifying the gradient through the curvature matrix. There have been many attempts to use secondorder optimization methods for training deep neural networks. In this work, inspired by diagonal approximations and factored approximations such as Kronecker-factored Approximate Curvature (KFAC), we propose a new approximation to the Fisher information matrix (FIM) called Trace-restricted Kronecker-factored Approximate Curvature (TKFAC), which can hold the certain trace relationship between the exact and the approximate FIM. In TKFAC, we decompose each block of the approximate FIM as a Kronecker product of two smaller matrices and scaled by a coefficient related to trace. We theoretically analyze TKFAC’s approximation error and give an upper bound of it. We also propose a new damping technique for TKFAC on convolutional neural networks to maintain the superiority of second-order optimization methods during training. Experiments show that our method has better performance compared with several state-of-the-art algorithms on some deep network architectures.

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AAAI Conference 2021 Conference Paper

THOR, Trace-based Hardware-driven Layer-Oriented Natural Gradient Descent Computation

Mengyun Chen
Kaixin Gao
Xiaolei Liu
Zidong Wang
Ningxi Ni
Qian Zhang
Lei Chen
Chao Ding

It is well-known that second-order optimizer can accelerate the training of deep neural networks, however, the huge computation cost of second-order optimization makes it impractical to apply in real practice. In order to reduce the cost, many methods have been proposed to approximate a second-order matrix. Inspired by KFAC, we propose a novel Trace-based Hardware-driven layer-ORiented Natural Gradient Descent Computation method, called THOR, to make the second-order optimization applicable in the real application models. Specifically, we gradually increase the update interval and use the matrix trace to determine which blocks of Fisher Information Matrix (FIM) need to be updated. Moreover, by resorting the power of hardware, we have designed a hardware-driven approximation method for computing FIM to achieve better performance. To demonstrate the effectiveness of THOR, we have conducted extensive experiments. The results show that training ResNet-50 on ImageNet with THOR only takes 66. 7 minutes to achieve a top-1 accuracy of 75. 9 % under an 8 Ascend 910 environment with MindSpore, a new deep learning computing framework. Moreover, with more computational resources, THOR can only takes 2. 7 minutes to 75. 9 % with 256 Ascend 910.

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AAAI Conference 2020 Conference Paper

Weighted-Sampling Audio Adversarial Example Attack

Xiaolei Liu
Kun Wan
Yufei Ding
Xiaosong Zhang
Qingxin Zhu

Recent studies have highlighted audio adversarial examples as a ubiquitous threat to state-of-the-art automatic speech recognition systems. Thorough studies on how to effectively generate adversarial examples are essential to prevent potential attacks. Despite many research on this, the efﬁciency and the robustness of existing works are not yet satisfactory. In this paper, we propose weighted-sampling audio adversarial examples, focusing on the numbers and the weights of distortion to reinforce the attack. Further, we apply a denoising method in the loss function to make the adversarial attack more imperceptible. Experiments show that our method is the ﬁrst in the ﬁeld to generate audio adversarial examples with low noise and high audio robustness at the minute timeconsuming level 1.

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