CV

Education

• Ph.D in Version Control Theory, GitHub University, 2018 (expected)
• M.S. in Jekyll, GitHub University, 2014
• B.S. in GitHub, GitHub University, 2012

Work experience

• Spring 2024: Academic Pages Collaborator
  • Github University
  • Duties includes: Updates and improvements to template
  • Supervisor: The Users
• Fall 2015: Research Assistant
  • Github University
  • Duties included: Merging pull requests
  • Supervisor: Professor Hub
• Summer 2015: Research Assistant
  • Github University
  • Duties included: Tagging issues
  • Supervisor: Professor Git

Skills

• Skill 1
• Skill 2
  • Sub-skill 2.1
  • Sub-skill 2.2
  • Sub-skill 2.3
• Skill 3

Publications

On-Policy RL Meets Off-Policy Experts: Harmonizing Supervised Fine-Tuning and Reinforcement Learning via Dynamic Weighting

W. Zhang, Y. Xie, Y. Sun, Y. Chen, G. Wang, Y. Li, B. Ding, and J. Zhou, “On-Policy RL Meets Off-Policy Experts: Harmonizing Supervised Fine-Tuning and Reinforcement Learning via Dynamic Weighting,” arxiv: 2508.11408.

Enhancing Latent Computation in Transformers with Latent Tokens

Y. Sun, Y. Chen, Y. Li, and B. Ding, “Enhancing latent computation in transformers with latent tokens,” submitted.

Learn How to Query from Unlabeled Data Streams in Federated Learning

Y. Sun, X. Li, T. Lin, and J. Zhang. (2024). "Learn How to Query from Unlabeled Data Streams in Federated Learning." AAAI 2025.

Exploring Selective Layer Fine-Tuning in Federated Learning

Y. Sun, Y. Xie, B. Ding, Y. Li, and J. Zhang, “Exploring selective layer fine-tuning in federated learning,” IEEE Int. Symp. Inf. Theory (ISIT), Michigan, USA, Jun. 2025.

How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning

Y. Sun, M. Kountouris, and J. Zhang, “How to collaborate: Towards maximizing the generalization performance in cross-silo federated learning,” accepted to IEEE Trans. Mobile Comput.

Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design

Y. Sun, J. Shao, Y. Mao, S. Li, and J. Zhang, “Stochastic coded federated learning: The- oretical analysis and incentive mechanism design,” IEEE Trans. Wireless Commun., vol. 23, no. 6, pp. 6623-6638, Jun. 2024.

Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning-the-Air Federated Learning

Y. Sun, Z. Lin, Y. Mao, S. Jin, J. Zhang, “Channel and gradient-importance aware de- vice scheduling for over-the-air federated learning,” IEEE Trans. Wireless Commun., vol. 23, no. 7, pp. 6905-6920, Jul. 2024.

MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates

Y. Sun, Y. Mao, and J. Zhang, “MimiC: Combating client dropouts in federated learning by mimicking central updates,” IEEE Trans. Mob. Comput., to appear.

DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing

J. Shao, Y. Sun, S. Li, and J. Zhang. (2022). "DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing." NeurIPS 2022.

Asynchronous Semi-Decentralized Federated Edge Learning for Heterogeneous Clients

Y. Sun, J. Shao, Y. Mao, and J. Zhang, “Asynchronous semi-decentralized federated edge learning for heterogenous clients,” IEEE Int. Conf. Commun. (ICC), Seoul, South Korea, May 2022.

Stochastic Coded Federated Learning with Convergence and Privacy Guarantees

Y. Sun, J. Shao, S. Li, Y. Mao, and J. Zhang, “Stochastic coded federated learning with convergence and privacy guarantees,” IEEE Int. Symp. Inf. Theory (ISIT), Espoo, Finland, Jun.-Jul. 2022.

Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity

Y. Sun, J. Shao, Y. Mao, J. Wang, and J. Zhang, “Semi-decentralized federated edge learning with data and device heterogeneity,” IEEE Trans. Netw. Service Manage., vol. 20, no. 2, pp. 1487-1501, Jun. 2023.

Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data

Y. Sun, J. Shao, Y. Mao, J. Wang, and J. Zhang, “Semi-decentralized federated edge learning for fast convergence on non-IID data,” IEEE Wireless Commun. Networking Conf. (WCNC), Austin, TX, USA, Apr. 2022.

Talks

Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity

September 01, 2022

Talk at ECE Future Leaders PG Seminar Series, HKUST, Hong Kong

Teaching

Teaching Assistant

Journal Reviewer

Conference Reviewer

Service and leadership

• Currently signed in to 43 different slack teams