Browsing by Author "Guo, Yi"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Distributed Consensus-based Weight Design for Cooperative Spectrum Sensing
    (IEEE, 2015-01) Zhang, Wenlin; Guo, Yi; Liu, Hongbo; Chen, Yingying; Wang, Zheng; Mitola, Joseph III; Department of Computer Information and Graphics Technology, School of Engineering and Technology
    In this paper, we study the distributed spectrum sensing in cognitive radio networks. Existing distributed consensus-based fusion algorithms only ensure equal gain combining of local measurements, whose performance may be incomparable to various centralized soft combining schemes. Motivated by this fact, we consider practical channel conditions and link failures, and develop new weighted soft measurement combining without a centralized fusion center. Following the measurement by its energy detector, each secondary user exchanges its own measurement statistics with its local one-hop neighbors, and chooses the information exchanging rate according to the measurement channel condition, e.g., the signal-to-noise ratio (SNR). We rigorously prove the convergence of the new consensus algorithm, and show all secondary users hold the same global decision statistics from the weighted soft measurement combining throughout the network. We also provide distributed optimal weight design under uncorrelated measurement channels. The convergence rate of the consensus iteration is given under the assumption that each communication link has an independent probability to fail, and the upper bound of the iteration number of the $ \epsilon$ -convergence is explicitly given as a function of system parameters. Simulation results show significant improvement of the sensing performance compared to existing consensus-based approaches, and the performance of the distributed weighted design is comparable to the centralized weighted combining scheme.
  • Thumbnail Image
    Item
    Federated learning with multi‐cohort real‐world data for predicting the progression from mild cognitive impairment to Alzheimer's disease
    (Wiley, 2025) Pan, Jinqian; Fan, Zhengkang; Smith, Glenn E.; Guo, Yi; Bian, Jiang; Xu, Jie; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Introduction: Leveraging routinely collected electronic health records (EHRs) from multiple health-care institutions, this approach aims to assess the feasibility of using federated learning (FL) to predict the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). Methods: We analyzed EHR data from the OneFlorida+ consortium, simulating six sites, and used a long short-term memory (LSTM) model with a federated averaging (FedAvg) algorithm. A personalized FL approach was used to address between-site heterogeneity. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and feature importance techniques. Results: Of 44,899 MCI patients, 6391 progressed to AD. FL models achieved a 6% improvement in AUC compared to local models. Key predictive features included body mass index, vitamin B12, blood pressure, and others. Discussion: FL showed promise in predicting AD progression by integrating heterogeneous data across multiple institutions while preserving privacy. Despite limitations, it offers potential for future clinical applications. Highlights: We applied long short-term memory and federated learning (FL) to predict mild cognitive impairment to Alzheimer's disease progression using electronic health record data from multiple institutions. FL improved prediction performance, with a 6% increase in area under the receiver operating characteristic curve compared to local models. We identified key predictive features, such as body mass index, vitamin B12, and blood pressure. FL shows effectiveness in handling data heterogeneity across multiple sites while ensuring data privacy. Personalized and pooled FL models generally performed better than global and local models.
  • Thumbnail Image
    Item
    Supplementary Material to “Distributed Consensus-based Weight Design for Cooperative Spectrum Sensing”
    (IEEE, 2015-01) Zhang, Wenlin; Guo, Yi; Liu, Hongbo; Chen, Yingying; Wang, Zheng; Mitola, Joseph III; Department of Computer Information and Graphics Technology, School of Engineering and Technology
    Abstract—This material is a supplement to the paper “Distributed Consensus-based Weight Design for Cooperative Spectrum Sensing”. Section 1 offers related literature review on cooperative spectrum sensing and consensus algorithms. Section 2 presents related notations and models of the consensus-based graph theory. Section 3 offers further analysis of the proposed spectrum sensing scheme including detection threshold settings and convergence properties in terms of detection performance. Section 4 presents the proofs for the convergence of the proposed consensus algorithm, and discusses the convergence of the proposed algorithm under random link failure network models. Section 5 shows additional simulation results.