What is reasonable is real; that which is real is reasonable.
----Georg Wilhelm Friedrich Hegel
YUXI ZHAO
Ph.D. advised by Dr. Xiaowen Gong in the Department of Electrical and Computer Engineering, Auburn University
Result-driven researcher with a strong background in federated learning and data crowdsourcing. Skilled at theoretical analysis, truthful incentive mechanism design, differential privacy, and multi-armed bandits problems. Solid mathematical background and coding ability. Effective communicator and team leader with the ability to manage collaborative projects and present works to all-level audiences.
Email: yzz0171@auburn.edu
Research interestes
Quality-Aware Data Crowdsourcing; Wireless Federated learning; Wireless Communication; Adaptive Pulse Compression
Current work
Quality-Aware Data Crowdsourcing; Wireless Federated learning
News
- 2022. 12 I graduated with a Ph.D. and a Master's degree from Auburn University.
- 2022. 12 Our paper titled "Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling" has been accepted by IEEE INFOCOM 2023.
- 2022. 05 I serve as a student volunteer at INFOCOM 2022.
- 2022. 03 I serve as a graduate student judge at Auburn Research: Student Symposium 2022.
- 2021. 11 Our paper titled "Data Poisoning Attacks and Defenses in Dynamic Crowdsourcing with Online Data Quality Learning" has been accepted by IEEE Transactions on Mobile Computing.
- 2021. 07 Our papers titled "Quality-Aware Distributed Computation for Cost-Effective Non-Convex and Asynchronous Wireless Federated Learning"(1st author) and "Quality-Aware Distributed Computation and Communication Scheduling for Fast Convergent Wireless Federated Learning"(2nd author) have been accepted by WiOpt 2021: 19th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks.
- 2021. 03 I attend the Auburn Research: Student Symposium 2021 and present our work "Quality-Aware Distributed Computation and User Selection for Cost-Effective Federated Learning".
- 2021. 03 I was selected for an INFOCOM 2021 Student Conference Award.
- 2021. 01 Our paper titled "Quality-Aware Distributed Computation and User Selection for Cost-Effective Federated Learning" has been accepted by FOGML 2021: The First International INFOCOM Workshop on Distributed Machine Learning and Fog Networks.
- 2020. 11 Our paper titled "Privacy-Preserving Incentive Mechanisms for Truthful Data Quality in Data Crowdsourcing" has been accepted by IEEE Transactions on Mobile Computing.
- 2020. 04 I am the lab instructor of Digital System Design (ELEC4200) in 2020 Fall.
- 2020. 02 I am a volunteer on E-day 2020.
- 2019. 12 I am the lab instructor of Digital System Design (ELEC4200) in 2020 Spring.
- 2019. 11 I give a talk on "Incentive Mechanism for Truthful Data Quality in Data Crowdsourcing" at the Wireless Seminar.
- 2019. 11 I attend the 2019 Fall Graduate Engineering Research Showcase and presented our work "Privacy-Preserving Incentive Mechanism for Truthful Data Quality in Data Crowdsourcing".
-2019. 11 I present our work "Privacy-Preserving Incentive Mechanism for Truthful Data Quality in Data Crowdsourcing" on the poster session of Fiber Wireless Integration and Networking (FiWIN).
- 2019. 04 I attend the Auburn Research: Student Symposium 2019 and present our work "Truthful Quality-Aware Data Crowdsensing for Machine Learning".
-2019. 04 Our paper titled "Truthful Quality-Aware Data Crowdsensing for Machine Learning" has been accepted by SECON’19.