MINDFL: Mitigating the Impact of Imbalanced and Noisy-Labeled Data in Federated Learning with Quality and Fairness-Aware Client Selection

Recommended citation: Chaoyu Zhang, Ning Wang, Shanghao Shi, Changlai Du, Wenjing Lou, Y. Thomas Hou. "MINDFL: Mitigating the Impact of Imbalanced and Noisy-Labeled Data in Federated Learning with Quality and Fairness-Aware Client Selection." IEEE MILCOM 2023.