Institution profile

Beijing Jiao Tong University

Academic institutionasia · cn
Official website
Research library729linked papers
Opportunities0open roles
Selected work

Representative Papers

Measurement-Based Modeling and Analysis of UAV Air-Ground Channels at 1 and 4 GHz

Jul 23, 2019IEEE Antennas and Wireless Propagation Letters

Existing vertical-dimension channel models for unmanned aerial vehicle (UAV) air-to-ground communications suffer from insufficient accuracy, particularly in characterizing height-dependent propagation effects. Method: This study conducts extensive field measurements at 1 GHz and 4 GHz across line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios, systematically quantifying large-scale path loss, shadow fading (modeled via log-normal distribution), and small-scale fading (validated against Rayleigh and Rician distributions). Contribution/Results: We propose, for the first time, a height-dependent path loss model that explicitly incorporates UAV flight altitude as a key parameter, and jointly characterize vertical-direction propagation specificity and fading statistics. The resulting high-fidelity air-to-ground channel model significantly improves link budget prediction accuracy and coverage performance assessment reliability. It provides a reproducible, scalable empirical foundation and modeling paradigm for low-altitude communication network design and optimization.

49 citations2 influentialRead paper

TransFR: Transferable Federated Recommendation with Pre-trained Language Models

Feb 02, 2024arXiv.org

Traditional federated recommendation systems (FRS) suffer from three critical bottlenecks: poor cross-domain transferability, ineffectiveness under cold-start conditions, and privacy leakage. To address these challenges, this paper proposes the first transferable framework integrating general-purpose textual representations with federated learning. Our method leverages pre-trained language models (e.g., BERT) to generate domain-agnostic item semantic embeddings—eliminating reliance on discrete item IDs—and jointly optimizes a federated fine-tuning procedure with locally personalized prediction heads to enhance cold-start robustness. Furthermore, we incorporate differential privacy into the training process to ensure user-level privacy protection. Extensive experiments on multiple benchmark datasets demonstrate that our approach significantly outperforms state-of-the-art methods, achieving substantial improvements in recommendation accuracy, cross-domain transferability, and cold-start performance, while rigorously preserving privacy guarantees.

4 citations2 influentialRead paper

TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning

Sep 01, 2024IEEE Transactions on Dependable and Secure Computing

Malicious aggregators in multi-party federated learning pose severe gradient leakage and privacy risks. Method: This paper introduces Threshold Fully Homomorphic Encryption (TFHE) into secure aggregation for the first time, proposing a decentralized privacy-preserving training framework tolerant to a bounded number of malicious aggregators. It eliminates reliance on trusted third parties by integrating secure multi-party computation with formal verification, effectively countering novel disaggregation attacks. Contribution/Results: We provide rigorous theoretical proofs establishing strict differential privacy and collusion resistance. Empirical evaluation demonstrates that the framework maintains state-of-the-art model accuracy while reducing communication overhead by 29–45%, and delivers end-to-end privacy guarantees under diverse strong adversarial models, including active and adaptive adversaries.

3 citationsRead paper

Rationale-Grounded In-Context Learning for Time Series Reasoning with Multimodal Large Language Models

Jan 06, 2026arXiv.org

Existing multimodal large language models often rely on superficial pattern matching for time series reasoning, lacking the principled ability to connect temporal observations with downstream outcomes. To address this limitation, this work proposes RationaleTS, a rationale-based in-context learning framework that uniquely treats reasoning pathways as proactive guidance rather than post-hoc explanations. RationaleTS generates structured rationales conditioned on labels and introduces a hybrid retrieval mechanism that integrates temporal patterns with semantic context to incorporate relevant reasoning priors. Evaluated across time series tasks in three distinct domains, RationaleTS significantly outperforms current baselines, demonstrating its effectiveness and efficiency in enhancing the model’s capacity for principled, causal-style reasoning.

1 citationsRead paper

Efficient Training of Neural Fractional-Order Differential Equation via Adjoint Backpropagation

Mar 20, 2025

To address the high memory consumption and computational complexity arising from forward-mode differentiation in training Neural Fractional Differential Equations (Neural FDEs), this work introduces, for the first time, adjoint-based backpropagation into the Neural FDE training framework. By formulating and solving an augmented fractional-order adjoint equation, our method enables efficient time-reversed gradient computation, overcoming the scalability limitations of conventional forward-mode differentiation in large-scale settings. The approach integrates fractional calculus, the adjoint state method, and neural differential equation theory, and is compatible with mainstream numerical FDE solvers. Experiments on tasks such as graph representation learning demonstrate performance on par with baseline models, while reducing memory usage by over 60% and accelerating training by 2–3×. This advancement significantly enhances the feasibility of Neural FDEs for large-scale dynamical system modeling.

1 citationsRead paper
Recent publications

Latest Papers