50 citations · 135 across the 9 of their papers we have counts for
9 papers
MGSER-SAM: Memory-Guided Soft Experience Replay with Sharpness-Aware Optimization for Enhanced Continual Learning
Xingyu Li, Bo Tang
Deep neural networks suffer from the catastrophic forgetting problem in the field of continual learning (CL). To address this challenge, we propose MGSER-SAM, a novel memory replay…
AdaER: An Adaptive Experience Replay Approach for Continual Lifelong Learning
Xingyu Li, Bo Tang, Haifeng Li
Continual lifelong learning is an machine learning framework inspired by human learning, where learners are trained to continuously acquire new knowledge in a sequential manner. Ho…
G-Mix: A Generalized Mixup Learning Framework Towards Flat Minima
Xingyu Li, Bo Tang
Deep neural networks (DNNs) have demonstrated promising results in various complex tasks. However, current DNNs encounter challenges with over-parameterization, especially when the…
GeoDTR+: Toward generic cross-view geolocalization via geometric disentanglement
Xiaohan Zhang, Xingyu Li, Waqas Sultani +2
Cross-View Geo-Localization (CVGL) estimates the location of a ground image by matching it to a geo-tagged aerial image in a database. Recent works achieve outstanding progress on…
On the Convergence of Multi-Server Federated Learning with Overlapping Area
Zhe Qu, Xingyu Li, Jie Xu +3
Multi-server Federated learning (FL) has been considered as a promising solution to address the limited communication resource problem of single-server FL. We consider a typical mu…
Generalized Federated Learning via Sharpness Aware Minimization
Zhe Qu, Xingyu Li, Rui Duan +3
Federated Learning (FL) is a promising framework for performing privacy-preserving, distributed learning with a set of clients. However, the data distribution among clients often e…
LoMar: A Local Defense Against Poisoning Attack on Federated Learning
Xingyu Li, Zhe Qu, Shangqing Zhao +3
Federated learning (FL) provides a high efficient decentralized machine learning framework, where the training data remains distributed at remote clients in a network. Though FL en…
FedLGA: Towards System-Heterogeneity of Federated Learning via Local Gradient Approximation
Xingyu Li, Zhe Qu, Bo Tang +1
Federated Learning (FL) is a decentralized machine learning architecture, which leverages a large number of remote devices to learn a joint model with distributed training data. Ho…
Stragglers Are Not Disaster: A Hybrid Federated Learning Algorithm with Delayed Gradients
Xingyu Li, Zhe Qu, Bo Tang +1
Federated learning (FL) is a new machine learning framework which trains a joint model across a large amount of decentralized computing devices. Existing methods, e.g., Federated A…