Chao Huang

Orcid: 0000-0003-0070-2160

Affiliations:
  • UC Davis, Department of Computer Science, CA, USA
  • Chinese University of Hong Kong, Department of Information Engineering, Hong Kong


According to our database1, Chao Huang authored at least 23 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2024
Incentivizing Efficient Label Denoising in Federated Learning.
IEEE Internet Things J., December, 2024

Promoting Collaboration in Cross-Silo Federated Learning: Challenges and Opportunities.
IEEE Commun. Mag., April, 2024

Duopoly Business Competition in Cross-Silo Federated Learning.
IEEE Trans. Netw. Sci. Eng., 2024

When Federated Learning Meets Oligopoly Competition: Stability and Model Differentiation.
IEEE Internet Things J., 2024

Convergence Analysis of Split Federated Learning on Heterogeneous Data.
CoRR, 2024

Incentivizing Participation in SplitFed Learning: Convergence Analysis and Model Versioning.
Proceedings of the 44th IEEE International Conference on Distributed Computing Systems, 2024

An Accuracy-Shaping Mechanism for Competitive Distributed Learning.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2024, 2024

2023
Online Crowd Learning Through Strategic Worker Reports.
IEEE Trans. Mob. Comput., September, 2023

Strategic Information Revelation Mechanism in Crowdsourcing Applications Without Verification.
IEEE Trans. Mob. Comput., May, 2023

An Online Inference-Aided Incentive Framework for Information Elicitation Without Verification.
IEEE J. Sel. Areas Commun., April, 2023

On the Impact of Label Noise in Federated Learning.
Proceedings of the 21st International Symposium on Modeling and Optimization in Mobile, 2023

Information Elicitation from Decentralized Crowd Without Verification.
Proceedings of the 21st International Symposium on Modeling and Optimization in Mobile, 2023

Federated Learning in Competitive EV Charging Market.
Proceedings of the IEEE PES Innovative Smart Grid Technologies Europe, 2023

Incentive Mechanism Design for Distributed Ensemble Learning.
Proceedings of the IEEE Global Communications Conference, 2023

2022
Eliciting Information From Heterogeneous Mobile Crowdsourced Workers Without Verification.
IEEE Trans. Mob. Comput., 2022

Using Truth Detection to Incentivize Workers in Mobile Crowdsourcing.
IEEE Trans. Mob. Comput., 2022

Quantifying the Impact of Label Noise on Federated Learning.
CoRR, 2022

Cross-Silo Federated Learning: Challenges and Opportunities.
CoRR, 2022

Incentivizing Data Contribution in Cross-Silo Federated Learning.
CoRR, 2022

2021
Strategic Information Revelation in Crowdsourcing Systems Without Verification.
Proceedings of the 40th IEEE Conference on Computer Communications, 2021

2020
Online Crowd Learning with Heterogeneous Workers via Majority Voting.
Proceedings of the 18th International Symposium on Modeling and Optimization in Mobile, 2020

2019
Crowdsourcing with Heterogeneous Workers in Social Networks.
Proceedings of the 2019 IEEE Global Communications Conference, 2019

Incentivizing Crowdsourced Workers via Truth Detection.
Proceedings of the 2019 IEEE Global Conference on Signal and Information Processing, 2019


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