most citedFederated Multi-Task Learning on Non-IID Data Silos: An Experimental Study

2 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

eess.IV2024

Discretized Gaussian Representation for Tomographic Reconstruction

Shaokai Wu, Yuxiang Lu, Yapan Guo +9

Computed Tomography (CT) enables detailed cross-sectional imaging but continues to face challenges in balancing reconstruction quality and computational efficiency. While deep lear…

cs.CV2024

Task Indicating Transformer for Task-conditional Dense Predictions

Yuxiang Lu, Shalayiding Sirejiding, Bayram Bayramli +3

The task-conditional model is a distinctive stream for efficient multi-task learning. Existing works encounter a critical limitation in learning task-agnostic and task-specific rep…

cs.CV2024

YOLO-MED : Multi-Task Interaction Network for Biomedical Images

Suizhi Huang, Shalayiding Sirejiding, Yuxiang Lu +4

Object detection and semantic segmentation are pivotal components in biomedical image analysis. Current single-task networks exhibit promising outcomes in both detection and segmen…

cs.LG20242 cited

Federated Multi-Task Learning on Non-IID Data Silos: An Experimental Study

Yuwen Yang, Yuxiang Lu, Suizhi Huang +3

The innovative Federated Multi-Task Learning (FMTL) approach consolidates the benefits of Federated Learning (FL) and Multi-Task Learning (MTL), enabling collaborative model traini…

cs.CV2023

FedHCA: Towards Hetero-Client Federated Multi-Task Learning

Yuxiang Lu, Suizhi Huang, Yuwen Yang +3

Federated Learning (FL) enables joint training across distributed clients using their local data privately. Federated Multi-Task Learning (FMTL) builds on FL to handle multiple tas…