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Researcher profile

Qun Li

Qun Li contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Machine LearningArtificial IntelligenceCryptography and SecurityDistributed, Parallel, and Cluster Computingquant-pheess.SP

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Published work

7 published item(s)

preprint2026arXiv

Tracking Large-scale Shared Bikes with Inertial Motion Learning in GNSS Blocked Environments

Although Global Navigation Satellite Systems (GNSS) provide a general solution for bike tracking outdoors, there still exist complex riding environments where only inertial navigation systems work, such as urban canyons. Despite decades of research, localization using only low-cost inertial sensors still faces challenges such as cumulative drifts and poor robustness caused by filtering methods. Furthermore, sensors such as visual and LiDAR could provide reliable measurements, but they are not suitable for large-scale deployment. In this paper, we propose an inertial tracking framework that integrates bicycle mechanical constraints with a mixture-of-experts model. Specifically, we leverage multiple expert modules to capture shared representations and weight them through the gating mechanism, thus improving multi-task learning performance and enabling uncertainty-aware trajectory estimation. Furthermore, based on the mechanical transmission between the pedal and the rear wheel of a bike, we explore the intrinsic relationship between the rider's periodic pedalling behaviors and acceleration variations, and convert such patterns into bike's wheel speed for dynamic calibration. Experiments with real-world riding data from shared bikes of the DiDi ride-hailing platform demonstrate that our system improves the accuracy of baselines by at least 12%, with wheel speed errors below 0.5 m/s at 95-percentile.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks

Variational quantum algorithms (VQAs) have recently received significant attention from the research community due to their promising performance in Noisy Intermediate-Scale Quantum computers (NISQ). However, VQAs run on parameterized quantum circuits (PQC) with randomly initialized parameters are characterized by barren plateaus (BP) where the gradient vanishes exponentially in the number of qubits. In this paper, we first review quantum natural gradient (QNG), which is one of the most popular algorithms used in VQA, from the classical first-order optimization point of view. Then, we proposed a \underline{L}ook \underline{A}round \underline{W}arm-\underline{S}tart QNG (LAWS) algorithm to mitigate the widespread existing BP issues. LAWS is a combinatorial optimization strategy taking advantage of model parameter initialization and fast convergence of QNG. LAWS repeatedly reinitializes parameter search space for the next iteration parameter update. The reinitialized parameter search space is carefully chosen by sampling the gradient close to the current optimal. Moreover, we present a unified framework (WS-SGD) for integrating parameter initialization techniques into the optimizer. We provide the convergence proof of the proposed framework for both convex and non-convex objective functions based on Polyak-Lojasiewicz (PL) condition. Our experiment results show that the proposed algorithm could mitigate the BP and have better generalization ability in quantum classification problems.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

MUD-PQFed: Towards Malicious User Detection in Privacy-Preserving Quantized Federated Learning

Federated Learning (FL), a distributed machine learning paradigm, has been adapted to mitigate privacy concerns for customers. Despite their appeal, there are various inference attacks that can exploit shared-plaintext model updates to embed traces of customer private information, leading to serious privacy concerns. To alleviate this privacy issue, cryptographic techniques such as Secure Multi-Party Computation and Homomorphic Encryption have been used for privacy-preserving FL. However, such security issues in privacy-preserving FL are poorly elucidated and underexplored. This work is the first attempt to elucidate the triviality of performing model corruption attacks on privacy-preserving FL based on lightweight secret sharing. We consider scenarios in which model updates are quantized to reduce communication overhead in this case, where an adversary can simply provide local parameters outside the legal range to corrupt the model. We then propose the MUD-PQFed protocol, which can precisely detect malicious clients performing attacks and enforce fair penalties. By removing the contributions of detected malicious clients, the global model utility is preserved to be comparable to the baseline global model without the attack. Extensive experiments validate effectiveness in maintaining baseline accuracy and detecting malicious clients in a fine-grained manner

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

QuantumFed: A Federated Learning Framework for Collaborative Quantum Training

With the fast development of quantum computing and deep learning, quantum neural networks have attracted great attention recently. By leveraging the power of quantum computing, deep neural networks can potentially overcome computational power limitations in classic machine learning. However, when multiple quantum machines wish to train a global model using the local data on each machine, it may be very difficult to copy the data into one machine and train the model. Therefore, a collaborative quantum neural network framework is necessary. In this article, we borrow the core idea of federated learning to propose QuantumFed, a quantum federated learning framework to have multiple quantum nodes with local quantum data train a mode together. Our experiments show the feasibility and robustness of our framework.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Scalable Quantum Neural Networks for Classification

Many recent machine learning tasks resort to quantum computing to improve classification accuracy and training efficiency by taking advantage of quantum mechanics, known as quantum machine learning (QML). The variational quantum circuit (VQC) is frequently utilized to build a quantum neural network (QNN), which is a counterpart to the conventional neural network. Due to hardware limitations, however, current quantum devices only allow one to use few qubits to represent data and perform simple quantum computations. The limited quantum resource on a single quantum device degrades the data usage and limits the scale of the quantum circuits, preventing quantum advantage to some extent. To alleviate this constraint, we propose an approach to implementing a scalable quantum neural network (SQNN) by utilizing the quantum resource of multiple small-size quantum devices cooperatively. In an SQNN system, several quantum devices are used as quantum feature extractors, extracting local features from an input instance in parallel, and a quantum device works as a quantum predictor, performing prediction over the local features collected through classical communication channels. The quantum feature extractors in the SQNN system are independent of each other, so one can flexibly use quantum devices of varying sizes, with larger quantum devices extracting more local features. Especially, the SQNN can be performed on a single quantum device in a modular fashion. Our work is exploratory and carried out on a quantum system simulator using the TensorFlow Quantum library. The evaluation conducts a binary classification on the MNIST dataset. It shows that the SQNN model achieves a comparable classification accuracy to a regular QNN model of the same scale. Furthermore, it demonstrates that the SQNN model with more quantum resources can significantly improve classification accuracy.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Towards Privacy-Preserving and Verifiable Federated Matrix Factorization

Recent years have witnessed the rapid growth of federated learning (FL), an emerging privacy-aware machine learning paradigm that allows collaborative learning over isolated datasets distributed across multiple participants. The salient feature of FL is that the participants can keep their private datasets local and only share model updates. Very recently, some research efforts have been initiated to explore the applicability of FL for matrix factorization (MF), a prevalent method used in modern recommendation systems and services. It has been shown that sharing the gradient updates in federated MF entails privacy risks on revealing users' personal ratings, posing a demand for protecting the shared gradients. Prior art is limited in that they incur notable accuracy loss, or rely on heavy cryptosystem, with a weak threat model assumed. In this paper, we propose VPFedMF, a new design aimed at privacy-preserving and verifiable federated MF. VPFedMF provides guarantees on the confidentiality of individual gradient updates through lightweight and secure aggregation. Moreover, VPFedMF ambitiously and newly supports correctness verification of the aggregation results produced by the coordinating server in federated MF. Experiments on a real-world movie rating dataset demonstrate the practical performance of VPFedMF in terms of computation, communication, and accuracy.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Spatio-Temporal Hierarchical Adaptive Dispatching for Ridesharing Systems

Nowadays, ridesharing has become one of the most popular services offered by online ride-hailing platforms (e.g., Uber and Didi Chuxing). Existing ridesharing platforms adopt the strategy that dispatches orders over the entire city at a uniform time interval. However, the uneven spatio-temporal order distributions in real-world ridesharing systems indicate that such an approach is suboptimal in practice. Thus, in this paper, we exploit adaptive dispatching intervals to boost the platform's profit under a guarantee of the maximum passenger waiting time. Specifically, we propose a hierarchical approach, which generates clusters of geographical areas suitable to share the same dispatching intervals, and then makes online decisions of selecting the appropriate time instances for order dispatch within each spatial cluster. Technically, we prove the impossibility of designing constant-competitive-ratio algorithms for the online adaptive interval problem, and propose online algorithms under partial or even zero future order knowledge that significantly improve the platform's profit over existing approaches. We conduct extensive experiments with a large-scale ridesharing order dataset, which contains all of the over 3.5 million ridesharing orders in Beijing, China, received by Didi Chuxing from October 1st to October 31st, 2018. The experimental results demonstrate that our proposed algorithms outperform existing approaches.

0 citations0 reviewsNo code linkedOpen access