no code implementations • 25 May 2024 • Yongxin Guo, Lin Wang, Xiaoying Tang, Tao Lin
To demonstrate the effectiveness of the proposed Client2Vec method, we conduct three case studies that assess the impact of the client index on the FL training process.
no code implementations • 23 May 2024 • Yongxin Guo, Zhenglin Cheng, Xiaoying Tang, Tao Lin
The Sparse Mixture of Experts (SMoE) has been widely employed to enhance the efficiency of training and inference for Transformer-based foundational models, yielding promising results.
no code implementations • 22 May 2024 • Yongxin Guo, Jingyu Liu, Mingda Li, Xiaoying Tang, Xi Chen, Bo Zhao
Video Temporal Grounding (VTG) focuses on accurately identifying event timestamps within a particular video based on a linguistic query, playing a vital role in downstream tasks such as video browsing and editing.
1 code implementation • 11 Jan 2024 • Kunpeng Qiu, Zhiying Zhou, Yongxin Guo
Accurate lesion classification in Wireless Capsule Endoscopy (WCE) images is vital for early diagnosis and treatment of gastrointestinal (GI) cancers.
no code implementations • 9 Oct 2023 • Yongxin Guo, Xiaoying Tang, Tao Lin
To this end, this paper presents a comprehensive investigation into current clustered FL methods and proposes a four-tier framework, namely HCFL, to encompass and extend existing approaches.
no code implementations • 29 Jan 2023 • Yongxin Guo, Xiaoying Tang, Tao Lin
In this paper, we identify the learning challenges posed by the simultaneous occurrence of diverse distribution shifts and propose a clustering principle to overcome these challenges.
1 code implementation • 26 May 2022 • Yongxin Guo, Xiaoying Tang, Tao Lin
As a remedy, we propose FedBR, a novel unified algorithm that reduces the local learning bias on features and classifiers to tackle these challenges.
no code implementations • 5 May 2022 • Weichen Fan, Yuanbo Yang, Kunpeng Qiu, Shuo Wang, Yongxin Guo
Therefore, to address the generalization problem in GI(Gastrointestinal) endoscopy, we propose a multi-domain GI dataset and a light, plug-in block called InvNorm(Invertible Normalization), which could achieve a better generalization performance in any structure.
no code implementations • 25 Dec 2021 • Yongxin Guo, Tao Lin, Xiaoying Tang
Federated Learning (FL) is a learning paradigm that protects privacy by keeping client data on edge devices.