7 citations · 8 across the 5 of their papers we have counts for
6 papers
Over-the-Air Federated Multi-Task Learning via Model Sparsification and Turbo Compressed Sensing
Haoming Ma, Xiaojun Yuan, Zhi Ding +2
To achieve communication-efficient federated multitask learning (FMTL), we propose an over-the-air FMTL (OAFMTL) framework, where multiple learning tasks deployed on edge devices s…
GPU-Net: Lightweight U-Net with more diverse features
Heng Yu, Di Fan, Weihu Song
Image segmentation is an important task in the medical image field and many convolutional neural networks (CNNs) based methods have been proposed, among which U-Net and its variant…
On the Fairness of Swarm Learning in Skin Lesion Classification
Di Fan, Yifan Wu, Xiaoxiao Li
in healthcare. However, the existing AI model may be biased in its decision marking. The bias induced by data itself, such as collecting data in subgroups only, can be mitigated by…
Over-the-Air Federated Multi-Task Learning
Haoming Ma, Xiaojun Yuan, Dian Fan +3
In this letter, we introduce over-the-air computation into the communication design of federated multi-task learning (FMTL), and propose an over-the-air federated multi-task learni…
Temporal-Structure-Assisted Gradient Aggregation for Over-the-Air Federated Edge Learning
Dian Fan, Xiaojun Yuan, Ying-Jun Angela Zhang
In this paper, we investigate over-the-air model aggregation in a federated edge learning (FEEL) system. We introduce a Markovian probability model to characterize the intrinsic te…
Apparent Liquid Permeability in Mixed-Wet Shale Permeable Media
Dian Fan, Amin Ettehadtavakkol, Wendong Wang
Apparent liquid permeability (ALP) in ultra-confined permeable media is primarily governed by the pore confinement and fluid-rock interactions. A new ALP model is required to predi…