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Published:
- 10/12/2024: Our paper on active data querying in FL was accepted to AAAI 2025! Thanks to Xinran, Dr. Tao Lin, and Prof. Zhang! The paper and code are public now.
portfolio
In Kennedy Town, Hong Kong, 2022
In Nice, France, June 2023
In Hong Kong, May 2024
Dinner after my PhD defense
In Hangzhou, China, Aug. 2024
Dinner for celebrating my birthday with many friends in DAIL
HKUST, Hong Kong, Nov. 2024
Graduation Ceremony 
publications
Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data
Published in IEEE WCNC 2022, 2021
Recommended citation: 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.
Paper
Asynchronous Semi-Decentralized Federated Edge Learning for Heterogeneous Clients
Published in IEEE ICC 2022, 2021
Recommended citation: 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.
Paper
Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity
Published in IEEE Transactions on Network and Service Management, 2021
Recommended citation: 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.
Paper | Introduction | Codes
Stochastic Coded Federated Learning with Convergence and Privacy Guarantees
Published in IEEE ISIT 2022, 2022
Recommended citation: 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.
Paper | Introduction | Codes
DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing
Published in NeurIPS 2022, 2022
Recommended citation: 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.
Paper | Introduction | Codes
MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates
Published in IEEE Transactions on Mobile Computing (CCF A), 2023
Recommended citation: Y. Sun, Y. Mao, and J. Zhang, “MimiC: Combating client dropouts in federated learning by mimicking central updates,” IEEE Trans. Mob. Comput., to appear.
Paper | Introduction | Codes
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning-the-Air Federated Learning
Published in IEEE Transactions on Wireless Communication on Wireless Communication, 2023
Recommended citation: 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.
Paper
Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design
Published in IEEE Transactions on Wireless Communication, 2023
Recommended citation: 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.
Paper
How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning
Published in IEEE Trans. Mobile Comput., 2024
Recommended citation: 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.
Paper
Exploring Selective Layer Fine-Tuning in Federated Learning
Published in Under review, 2024
Recommended citation: Y. Sun, Y. Xie, B. Ding, Y. Li, and J. Zhang, “Exploring selective layer fine-tuning in federated learning,” submitted.
Paper
Learn How to Query from Unlabeled Data Streams in Federated Learning
Published in AAAI 2025, 2024
The paper and code are public now.
Recommended citation: Y. Sun, X. Li, T. Lin, and J. Zhang. (2024). "Learn How to Query from Unlabeled Data Streams in Federated Learning." AAAI 2025.
Paper
talks
Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity
Published:
teaching
Conference Reviewer
IEEE GLOBECOM, IEEE SPAWC, Neurips, ICLR, ICML, AISTATS
Journal Reviewer
IEEE JSAC, IEEE TCOM (2022 Exemplary reviewer), IEEE TMLCN, IEEE TMC
Teaching Assistant
- 2021/22 Spring, TA of ELEC1100, HKUST
- 2022/23 Fall, TA of ELEC1010 and EESM5900, HKUST