Heterogeneous Federated Learning for Balancing Job Completion Time and Model Accuracy. - Ruiting Zhou; Ruobei Wang; Jieling Yu; Bo Li 001; Yuqing Li (ICPADS, 2022)

A Reinforcement Learning Approach for Minimizing Job Completion Time in Clustered Federated Learning. - Ruiting Zhou; Jieling Yu (ACM TUR-C, 2023)

ASFL: Adaptive Semi-asynchronous Federated Learning for Balancing Model Accuracy and Total Latency in Mobile Edge Networks. - Jieling Yu; Ruiting Zhou; Chen Chen 067; Bo Li 001; Fang Dong 001 (ICPP, 2023)

A Reinforcement Learning Approach for Minimizing Job Completion Time in Clustered Federated Learning. - Ruiting Zhou; Jieling Yu; Ruobei Wang; Bo Li 001; Jiacheng Jiang; Libing Wu (INFOCOM, 2023)

An Incentive Auction for Heterogeneous Client Selection in Federated Learning. - Jinlong Pang; Jieling Yu; Ruiting Zhou; John C. S. Lui (IEEE Trans. Mob. Comput., 2023)

Efficient Online DNN Inference with Continuous Learning in Edge Computing. - Yifan Zeng; Ruiting Zhou; Lei Jiao 002; Ziyi Han; Jieling Yu; Yue Ma (IWQoS, 2024)