2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.DC2024
Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics
Ming Xiang, Stratis Ioannidis, Edmund Yeh +2
Federated learning is a popular distributed learning approach for training a machine learning model without disclosing raw data. It consists of a parameter server and a possibly la…
cs.LG2023★ 2 cited
Towards Bias Correction of FedAvg over Nonuniform and Time-Varying Communications
Ming Xiang, Stratis Ioannidis, Edmund Yeh +2
Federated learning (FL) is a decentralized learning framework wherein a parameter server (PS) and a collection of clients collaboratively train a model via minimizing a global obje…
cs.CV2022
Towards Robust Video Object Segmentation with Adaptive Object Calibration
Xiaohao Xu, Jinglu Wang, Xiang Ming +1
In the booming video era, video segmentation attracts increasing research attention in the multimedia community. Semi-supervised video object segmentation (VOS) aims at segmenting…