activity
20212023
most citedFederated Adversarial Learning: A Framework with Convergence Analysis

8 citations · 16 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV2023

Energizing Federated Learning via Filter-Aware Attention

Ziyuan Yang, Zerui Shao, Huijie Huangfu +5

Federated learning (FL) is a promising distributed paradigm, eliminating the need for data sharing but facing challenges from data heterogeneity. Personalized parameter generation…

cs.CL2023

Transsion TSUP's speech recognition system for ASRU 2023 MADASR Challenge

Xiaoxiao Li, Gaosheng Zhang, An Zhu +4

This paper presents a speech recognition system developed by the Transsion Speech Understanding Processing Team (TSUP) for the ASRU 2023 MADASR Challenge. The system focuses on ada…

cs.LG20233 cited

FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation

Minghui Chen, Meirui Jiang, Qi Dou +2

Cross-silo federated learning (FL) enables the development of machine learning models on datasets distributed across data centers such as hospitals and clinical research laboratori…

cs.LG20228 cited

Federated Adversarial Learning: A Framework with Convergence Analysis

Xiaoxiao Li, Zhao Song, Jiaming Yang

Federated learning (FL) is a trending training paradigm to utilize decentralized training data. FL allows clients to update model parameters locally for several epochs, then share…

cs.CV20221 cited

Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection

Ziteng Cui, Yingying Zhu, Lin Gu +5

Image restoration algorithms such as super resolution (SR) are indispensable pre-processing modules for object detection in low quality images. Most of these algorithms assume the…

cs.CV2022

Backdoor Attack is a Devil in Federated GAN-based Medical Image Synthesis

Ruinan Jin, Xiaoxiao Li

Deep Learning-based image synthesis techniques have been applied in healthcare research for generating medical images to support open research. Training generative adversarial neur…