Publications

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

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

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

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

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

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

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

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

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 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

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