no code implementations • 14 Nov 2023 • Yuwei Wang, Runhan Li, Hao Tan, Xuefeng Jiang, Sheng Sun, Min Liu, Bo Gao, Zhiyuan Wu
By fusing the logits of the two models, the private weak learner can capture the variance of different data, regardless of their category.
no code implementations • 31 May 2023 • Anya Li, Bhala Ranganathan, Feng Pan, Mickey Zhang, Qianjun Xu, Runhan Li, Sethu Raman, Shail Paragbhai Shah, Vivienne Tang
Companies are using machine learning to solve real-world problems and are developing hundreds to thousands of features in the process.
1 code implementation • 14 Jan 2023 • Zhiyuan Wu, Sheng Sun, Yuwei Wang, Min Liu, Xuefeng Jiang, Runhan Li, Bo Gao
The increasing demand for intelligent services and privacy protection of mobile and Internet of Things (IoT) devices motivates the wide application of Federated Edge Learning (FEL), in which devices collaboratively train on-device Machine Learning (ML) models without sharing their private data.