Chao Huang
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Publications

  1. Convergence Analaysis of Split Federated Learning on Heterogeneous Data.
    Pengchao Han, Chao Huang, Gen Tian, Ming Tang, Xin Liu
    Conference on Neural Information Processing Systems (NeurIPS), December 2024.
  2. Coopetition in Heterogeneous Cross-Silo Federated Learning.
    Chao Huang, Justin Dachille, Xin Liu
    European Conference on Artificial Intelligence (ECAI), October 2024.
  3. An Accuracy-Shaping Mechanism for Competitive Distributed Learning.
    Chao Huang, Justin Dachille, Xin Liu
    International Conference on Artificial Neural Networks (ICANN), September 2024.
  4. Incentivizing Efficient Label Denoising in Federated Learning.
    Yizhou Yan, Xinyu Tang, Chao Huang, Ming Tang
    IEEE Internet of Things Journal (IoT-J), August 2024.
  5. Incentivizing Participation in SplitFed Learning: Convergence Analysis and Model Versioning.
    Pengchao Han, Chao Huang, Xingyan Shi, Jianwei Huang, Xin Liu
    IEEE International Conference on Distributed Computing Systems (ICDCS), July 2024.
  6. FedUV: Uniformity and Variance for Heterogeneous Federated Learning.
    Ha Min Son, Moon Hyun Kim, Tai-Myoung Chung, Chao Huang, Xin Liu
    IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), June 2024
  7. When Federated Learning Meets Oligopoly Competition: Stability and Model Differentiation.
    Chao Huang, Justin Dachille, Xin Liu
    IEEE Internet of Things Journal (IoT-J), May 2024.
  8. Promoting Collaborations in Cross-Silo Federated Learning: Challenges and Opportunities.
    Chao Huang, Ming Tang, Qian Ma, Jianwei Huang, and Xin Liu.
    IEEE Communications Magazine, December 2023.
  9. Price of Stability in Quality-Aware Federated Learning.
    Yizhou Yan, Xinyu Tang, Chao Huang, and Ming Tang.
    IEEE Global Communications Conference (GLOBECOM), December 2023.
  10. Incentive Mechanism Design for Distributed Ensemble Learning.
    Chao Huang, Pengchao Han, and Jianwei Huang.
    IEEE Global Communications Conference (GLOBECOM), December 2023.
  11. Infection Prediction in Swine Populations with Machine Learning.
    Avishai Halev, Beatriz Martınez-Lopez, Maria Clavijo, Carlos Gonzalez-Crespo, Jeonghoon Kim, Chao Huang, Rebecca Robbins, and Xin Liu.
    Scientific Reports, October 2023.
  12. Federated Learning in Competitive EV Charging Market.
    Chenxi Sun, Chao Huang, Biying Shou, and Jianwei Huang.
    IEEE PES Conference on Innovative Smart Grid Technologies (ISGT), Europe, October 2023
  13. On the Impact of Label Noise in Federated Learning.
    Shuqi Ke, Chao Huang, and Xin Liu.
    International Symposium on Modeling and Optimization in Mobile, Ad Hoc andWireless Networks (WiOpt), August 2023.
  14. Information Elicitation from Decentralized Crowd Without Verification.
    Kexin Chen, Chao Huang, and Jianwei Huang.
    WiOpt Workshop on Resource Allocation and Cooperation in Wireless Networks, August 2023.
  15. Duopoly Business Competition in Cross-Silo Federated Learning.
    Chao Huang, Shuqi Ke, and Xin Liu
    IEEE Transactions on Network Science and Engineering (TNSE), July 2023.
  16. Incentive Mechanism Design for Mobile Crowdsourcing Without Verification.
    Chao Huang, Haoran Yu, Jianwei Huang, and Randall Berry.
    Advances in Mobile Crowdsourcing: From Theory to Practice, July 2023
  17. Predicting Antimicrobial Resistance of Bacterial Pathogens using Time Series Analysis.
    Jeonghoon Kim, Ruwini Rupasinghe, Avisahi Halev, Chao Huang, Shahbaz Rezaei, Maria Jose Clavijo, Rebecca Claire Robbins, Beatriz Martínez-López, and Xin Liu.
    Frontiers in Microbiology, section Antimicrobials, Resistance and Chemotherapy, April 2023.
  18. Quantifying the Impact of Label Noise on Federated Learning.
    Shuqi Ke, Chao Huang, and Xin Liu.
    AAAI Workshop on Representation learning for Responsible Human-Centric AI, Feburary 2023.
  19. An Online Inference-Aided Incentive Framework for Information Elicitation Without Verification.
    Chao Huang, Haoran Yu, Jianwei Huang, and Randall Berry.
    IEEE Journal on Selected Areas in Communications (JSAC), Feburary 2023.
  20. Online Crowd Learning with Strategic Worker Reports.
    Chao Huang, Haoran Yu, Jianwei Huang, and Randall Berry.
    IEEE Transactions on Mobile Computing (TMC), May 2022.
  21. Strategic Information Revelation Mechanism in Crowdsourcing Applications Without Verification.
    Chao Huang, Haoran Yu, Jianwei Huang, and Randall Berry.
    IEEE Transactions on Mobile Computing (TMC), November 2021.
  22. Strategic Information Revelation in Crowdsourcing SystemsWithout Verification.
    Chao Huang, Haoran Yu, Jianwei Huang, and Randall Berry.
    IEEE International Conference on Computer Communications (INFOCOM), May 2021.
  23. Eliciting Information from Heterogeneous Mobile Crowdsourced Workers Without Verification.
    Chao Huang, Haoran Yu, Jianwei Huang, and Randall Berry.
    IEEE Transactions on Mobile Computing (TMC), Feburary 2021.
  24. Using Truth Detection to Incentivize Workers in Mobile Crowdsourcing.
    Chao Huang, Haoran Yu, Randall Berry, and Jianwei Huang.
    IEEE Transactions on Mobile Computing (TMC), October 2020.
  25. Online Crowd Learning with Heterogeneous Workers via Majority Voting.
    Chao Huang, Haoran Yu, Jianwei Huang, and Randall Berry.
    International Symposium on Modeling and Optimization in Mobile, Ad Hoc andWireless Networks (WiOpt), June 2020.
  26. Crowdsourcing with Heterogeneous Workers in Social Networks.
    Chao Huang, Haoran Yu, Jianwei Huang, and Randall Berry,
    IEEE Global Communications Conference (GLOBECOM), December 2019.
  27. Incentivizing Crowdsourced Workers via Truth Detection.
    Chao Huang, Haoran Yu, Jianwei Huang, and Randall Berry.
    IEEE Global Conference on Signal and Information Processing (GlobalSIP), November 2019