Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation
Fuente:
arXiv
Saved in:
| Main Authors: | Wu, Xinghao, Niu, Jianwei, Liu, Xuefeng, Shi, Mingjia, Zhu, Guogang, Tang, Shaojie |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
The Other Side of the Coin: Unveiling the Downsides of Model Aggregation in Federated Learning from a Layer-peeled Perspective
by: Zhu, Guogang, et al.
Published: (2025)
by: Zhu, Guogang, et al.
Published: (2025)
DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations
by: Zhu, Guogang, et al.
Published: (2024)
by: Zhu, Guogang, et al.
Published: (2024)
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank Decomposition
by: Wu, Xinghao, et al.
Published: (2024)
by: Wu, Xinghao, et al.
Published: (2024)
From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning
by: Wu, Xinghao, et al.
Published: (2026)
by: Wu, Xinghao, et al.
Published: (2026)
The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning
by: Wu, Xinghao, et al.
Published: (2024)
by: Wu, Xinghao, et al.
Published: (2024)
Enhancing Visual Representation with Textual Semantics: Textual Semantics-Powered Prototypes for Heterogeneous Federated Learning
by: Wu, Xinghao, et al.
Published: (2025)
by: Wu, Xinghao, et al.
Published: (2025)
Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learning
by: Zhu, Guogang, et al.
Published: (2024)
by: Zhu, Guogang, et al.
Published: (2024)
Why Go Full? Elevating Federated Learning Through Partial Network Updates
by: Wang, Haolin, et al.
Published: (2024)
by: Wang, Haolin, et al.
Published: (2024)
Learning to Optimize Job Shop Scheduling Under Structural Uncertainty
by: Zhang, Rui, et al.
Published: (2026)
by: Zhang, Rui, et al.
Published: (2026)
Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation
by: Guo, Wenkai, et al.
Published: (2025)
by: Guo, Wenkai, et al.
Published: (2025)
FedMP: Tackling Medical Feature Heterogeneity in Federated Learning from a Manifold Perspective
by: Zhou, Zhekai, et al.
Published: (2025)
by: Zhou, Zhekai, et al.
Published: (2025)
Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift
by: Chen, Junbao, et al.
Published: (2024)
by: Chen, Junbao, et al.
Published: (2024)
FedLF: Adaptive Logit Adjustment and Feature Optimization in Federated Long-Tailed Learning
by: Lu, Xiuhua, et al.
Published: (2024)
by: Lu, Xiuhua, et al.
Published: (2024)
Tackling Privacy Heterogeneity in Differentially Private Federated Learning
by: Xu, Ruichen, et al.
Published: (2026)
by: Xu, Ruichen, et al.
Published: (2026)
Tackling Noisy Clients in Federated Learning with End-to-end Label Correction
by: Jiang, Xuefeng, et al.
Published: (2024)
by: Jiang, Xuefeng, et al.
Published: (2024)
E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing
by: Zhou, Yuhao, et al.
Published: (2025)
by: Zhou, Yuhao, et al.
Published: (2025)
Feature-Aware One-Shot Federated Learning via Hierarchical Token Sequences
by: Liu, Shudong, et al.
Published: (2026)
by: Liu, Shudong, et al.
Published: (2026)
FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning
by: Ali, Abdulmoneam, et al.
Published: (2026)
by: Ali, Abdulmoneam, et al.
Published: (2026)
Two-Stage Feature Generation with Transformer and Reinforcement Learning
by: Gao, Wanfu, et al.
Published: (2025)
by: Gao, Wanfu, et al.
Published: (2025)
Tackling Noisy Labels with Network Parameter Additive Decomposition
by: Wang, Jingyi, et al.
Published: (2024)
by: Wang, Jingyi, et al.
Published: (2024)
XSub: Explanation-Driven Adversarial Attack against Blackbox Classifiers via Feature Substitution
by: Vu, Kiana, et al.
Published: (2024)
by: Vu, Kiana, et al.
Published: (2024)
Tackling Data Heterogeneity in Federated Learning via Loss Decomposition
by: Zeng, Shuang, et al.
Published: (2024)
by: Zeng, Shuang, et al.
Published: (2024)
Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity
by: Gu, Hanlin, et al.
Published: (2024)
by: Gu, Hanlin, et al.
Published: (2024)
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt Tuning
by: Zhu, Jiapeng, et al.
Published: (2024)
by: Zhu, Jiapeng, et al.
Published: (2024)
Understanding DNNs in Feature Interaction Models: A Dimensional Collapse Perspective
by: Wang, Jiancheng, et al.
Published: (2026)
by: Wang, Jiancheng, et al.
Published: (2026)
STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning
by: Chen, Zekai, et al.
Published: (2026)
by: Chen, Zekai, et al.
Published: (2026)
Tackling Feature and Sample Heterogeneity in Decentralized Multi-Task Learning: A Sheaf-Theoretic Approach
by: Issaid, Chaouki Ben, et al.
Published: (2025)
by: Issaid, Chaouki Ben, et al.
Published: (2025)
FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering
by: Guo, Yongxin, et al.
Published: (2023)
by: Guo, Yongxin, et al.
Published: (2023)
Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization
by: Huang, Xiaohan, et al.
Published: (2025)
by: Huang, Xiaohan, et al.
Published: (2025)
Personalized Federated Learning via Gaussian Generative Modeling
by: Hu, Peng, et al.
Published: (2026)
by: Hu, Peng, et al.
Published: (2026)
Personalized Federated Learning via Feature Distribution Adaptation
by: Mclaughlin, Connor J., et al.
Published: (2024)
by: Mclaughlin, Connor J., et al.
Published: (2024)
FedDiverse: Tackling Data Heterogeneity in Federated Learning with Diversity-Driven Client Selection
by: Németh, Gergely D., et al.
Published: (2025)
by: Németh, Gergely D., et al.
Published: (2025)
Exploiting Features and Logits in Heterogeneous Federated Learning
by: Chan, Yun-Hin, et al.
Published: (2022)
by: Chan, Yun-Hin, et al.
Published: (2022)
Tackling the Local Bias in Federated Graph Learning
by: Zhang, Binchi, et al.
Published: (2021)
by: Zhang, Binchi, et al.
Published: (2021)
Power Transform Revisited: Numerically Stable, and Federated
by: Xu, Xuefeng, et al.
Published: (2025)
by: Xu, Xuefeng, et al.
Published: (2025)
Prompt-Driven Feature Diffusion for Open-World Semi-Supervised Learning
by: Heidari, Marzi, et al.
Published: (2024)
by: Heidari, Marzi, et al.
Published: (2024)
Fairness of Classifiers in the Presence of Constraints between Features
by: Cooper, Martin C., et al.
Published: (2026)
by: Cooper, Martin C., et al.
Published: (2026)
Classifying Dental Care Providers Through Machine Learning with Features Ranking
by: Al-Batah, Mohammad Subhi, et al.
Published: (2025)
by: Al-Batah, Mohammad Subhi, et al.
Published: (2025)
An Aggregation-Free Federated Learning for Tackling Data Heterogeneity
by: Wang, Yuan, et al.
Published: (2024)
by: Wang, Yuan, et al.
Published: (2024)
EasyFS: an Efficient Model-free Feature Selection Framework via Elastic Transformation of Features
by: Lv, Jianming, et al.
Published: (2024)
by: Lv, Jianming, et al.
Published: (2024)
Similar Items
-
The Other Side of the Coin: Unveiling the Downsides of Model Aggregation in Federated Learning from a Layer-peeled Perspective
by: Zhu, Guogang, et al.
Published: (2025) -
DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations
by: Zhu, Guogang, et al.
Published: (2024) -
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank Decomposition
by: Wu, Xinghao, et al.
Published: (2024) -
From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning
by: Wu, Xinghao, et al.
Published: (2026) -
The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning
by: Wu, Xinghao, et al.
Published: (2024)