Chao Huang
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Publications

Book Chapter

  1. 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

Conference Papers (peer reviewed, including workshops)

  1. Decoupled Split Learning via Auxiliary Loss.
    Anower Zihad, Felix Owino, Ming Tang, Chao Huang
    INFOCOM Workshop on Intelligent Cloud Computing and Networking (ICCN), Feburary 2026.
  2. Tackling Biased Evaluators in Dueling Bandits.
    Ming Tang, Yuxuan Zhou, Chao Huang
    Conference on Neural Information Processing Systems (NeurIPS, spotlight), December 2025.
  3. Tackling Sequential Entanglement in Split Unlearning.
    Ashley Etheridge, Michelle Zhu, Xin Liu, Chao Huang
    International Conference on Multimedia Information Processing and Retrieval (MIPR), August 2025.
  4. CoCoI: Distributed Coded Inference System for Straggler Mitigation.
    Xing Liu, Chao Huang, Ming Tang
    International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt), May 2025.
  5. The Impact of Cut Layer Selection in Split Federated Learning.
    Justin Dachille, Chao Huang, Xin Liu
    AAAI Workhop on Federated Learning for Unbounded and Intelligent Decentralization (FLUID), Feburary 2025.
  6. 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.
  7. Coopetition in Heterogeneous Cross-Silo Federated Learning.
    Chao Huang, Justin Dachille, Xin Liu
    European Conference on Artificial Intelligence (ECAI), October 2024.
  8. An Accuracy-Shaping Mechanism for Competitive Distributed Learning.
    Chao Huang, Justin Dachille, Xin Liu
    International Conference on Artificial Neural Networks (ICANN), September 2024.
  9. 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), 2024.
  10. 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), 2024
  11. 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, 2023
  12. Price of Stability in Quality-Aware Federated Learning.
    Yizhou Yan, Xinyu Tang, Chao Huang, and Ming Tang.
    IEEE Global Communications Conference (GLOBECOM), 2023.
  13. Information Elicitation from Decentralized Crowd Without Verification.
    Kexin Chen, Chao Huang, and Jianwei Huang.
    WiOpt Workshop on Resource Allocation and Cooperation in Wireless Networks, 2023.
  14. Incentive Mechanism Design for Distributed Ensemble Learning.
    Chao Huang*, Pengchao Han*, and Jianwei Huang.
    IEEE Global Communications Conference (GLOBECOM), 2023.
  15. 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), 2023.
  16. Strategic Information Revelation in Crowdsourcing SystemsWithout Verification.
    Chao Huang, Haoran Yu, Jianwei Huang, and Randall Berry.
    IEEE International Conference on Computer Communications (INFOCOM), 2021.
  17. 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), 2020.
  18. Crowdsourcing with Heterogeneous Workers in Social Networks.
    Chao Huang, Haoran Yu, Jianwei Huang, and Randall Berry,
    IEEE Global Communications Conference (GLOBECOM), 2019.
  19. Incentivizing Crowdsourced Workers via Truth Detection.
    Chao Huang, Haoran Yu, Jianwei Huang, and Randall Berry.
    IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2019

Journal Papers

  1. An Empirical Study on Impact of Label Noise on Synthetic Tabular Data Generation.
    Jeonghoon Kim, Chao Huang, Xin Liu
    Machine Learning, January 2025.
  2. Incentivizing Efficient Label Denoising in Federated Learning.
    Yizhou Yan, Xinyu Tang, Chao Huang, Ming Tang
    IEEE Internet of Things Journal (IoT-J), August 2024.
  3. Promoting Collaborations in Cross-Silo Federated Learning: Challenges and Opportunities.
    Chao Huang, Ming Tang, Qian Ma, Jianwei Huang, and Xin Liu.
    IEEE Communications Magazine, 2023.
  4. 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, 2023.
  5. 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.
  6. 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.
  7. 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), Feb. 2023.
  8. Online Crowd Learning with Strategic Worker Reports.
    Chao Huang, Haoran Yu, Jianwei Huang, and Randall Berry.
    IEEE Transactions on Mobile Computing (TMC), May 2022.
  9. Strategic Information Revelation Mechanism in Crowdsourcing Applications Without Verification.
    Chao Huang, Haoran Yu, Jianwei Huang, and Randall Berry.
    IEEE Transactions on Mobile Computing (TMC), 2021.
  10. Eliciting Information from Heterogeneous Mobile Crowdsourced Workers Without Verification.
    Chao Huang, Haoran Yu, Jianwei Huang, and Randall Berry.
    IEEE Transactions on Mobile Computing (TMC), 2021.
  11. Using Truth Detection to Incentivize Workers in Mobile Crowdsourcing.
    Chao Huang, Haoran Yu, Randall Berry, and Jianwei Huang.
    IEEE Transactions on Mobile Computing (TMC), 2020.

Working Papers

  1. HOSL: Hybrid-Order Split Learning for Memory-Constrained Edge Training.
    Aakriti Lnu, Zhe Li, Dandan Liang, Chao Huang, Rui Li, Haibo Yang.
  2. Beyond-Backpropagation Training: Methods, Applications, and Perspectives.
    Rongguang Ye, Chenhao Ye, Chao Huang, Ming Tang, Yunhao Liu.
  3. FlowSpec: Continuous Pipelined Speculative Decoding for Efficient Distributed LLM Inference.
    Xing Liu, Lizhuo Luo, Ming Tang, Chao Huang, Xu Chen