19 citations · 38 across the 9 of their papers we have counts for
9 papers
Fine-Tuning Impairs the Balancedness of Foundation Models in Long-tailed Personalized Federated Learning
Shihao Hou, Chikai Shang, Zhiheng Yang +5
Personalized federated learning (PFL) with foundation models has emerged as a promising paradigm enabling clients to adapt to heterogeneous data distributions. However, real-world…
CAPT: Class-Aware Prompt Tuning for Federated Long-Tailed Learning with Vision-Language Model
Shihao Hou, Xinyi Shang, Shreyank N Gowda +4
Effectively handling the co-occurrence of non-IID data and long-tailed distributions remains a critical challenge in federated learning. While fine-tuning vision-language models (V…
Personalized Federated Learning on Heterogeneous and Long-Tailed Data via Expert Collaborative Learning
Fengling Lv, Xinyi Shang, Yang Zhou +3
Personalized Federated Learning (PFL) aims to acquire customized models for each client without disclosing raw data by leveraging the collective knowledge of distributed clients. H…
FediOS: Decoupling Orthogonal Subspaces for Personalization in Feature-skew Federated Learning
Lingzhi Gao, Zexi Li, Yang Lu +1
Personalized federated learning (pFL) enables collaborative training among multiple clients to enhance the capability of customized local models. In pFL, clients may have heterogen…
Federated Semi-Supervised Learning with Annotation Heterogeneity
Xinyi Shang, Gang Huang, Yang Lu +4
Federated Semi-Supervised Learning (FSSL) aims to learn a global model from different clients in an environment with both labeled and unlabeled data. Most of the existing FSSL work…
No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier
Zexi Li, Xinyi Shang, Rui He +2
Data heterogeneity is an inherent challenge that hinders the performance of federated learning (FL). Recent studies have identified the biased classifiers of local models as the ke…
Revisiting Weighted Aggregation in Federated Learning with Neural Networks
Zexi Li, Tao Lin, Xinyi Shang +1
In federated learning (FL), weighted aggregation of local models is conducted to generate a global model, and the aggregation weights are normalized (the sum of weights is 1) and p…
FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated Distillation
Xinyi Shang, Yang Lu, Yiu-ming Cheung +1
Federated learning provides a privacy guarantee for generating good deep learning models on distributed clients with different kinds of data. Nevertheless, dealing with non-IID dat…
Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features
Xinyi Shang, Yang Lu, Gang Huang +1
Federated learning (FL) provides a privacy-preserving solution for distributed machine learning tasks. One challenging problem that severely damages the performance of FL models is…