1 code implementation • 13 Oct 2023 • Mingjia Shi, Yuhao Zhou, Kai Wang, Huaizheng Zhang, Shudong Huang, Qing Ye, Jiangcheng Lv
Personalized FL (PFL) addresses this by synthesizing personalized models from a global model via training on local data.
no code implementations • ICCV 2023 • Yuhao Zhou, Mingjia Shi, Yuanxi Li, Qing Ye, Yanan sun, Jiancheng Lv
Reducing communication overhead in federated learning (FL) is challenging but crucial for large-scale distributed privacy-preserving machine learning.
no code implementations • 19 Nov 2022 • Mingjia Shi, Yuhao Zhou, Qing Ye, Jiancheng Lv
Federated learning (FL for simplification) is a distributed machine learning technique that utilizes global servers and collaborative clients to achieve privacy-preserving global model training without direct data sharing.
Ranked #1 on Image Classification on Fashion-MNIST (Accuracy metric)
1 code implementation • 23 Jul 2020 • Qing Ye, Yuhao Zhou, Mingjia Shi, Yanan sun, Jiancheng Lv
Specifically, the performance of each worker is evaluatedfirst based on the fact in the previous epoch, and then the batch size and datasetpartition are dynamically adjusted in consideration of the current performanceof the worker, thereby improving the utilization of the cluster.