Saved in:
| Main Authors: | Di, Hao, Yang, Yi, Ye, Haishan, Chang, Xiangyu |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.14337 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
An Enhanced Zeroth-Order Stochastic Frank-Wolfe Framework for Constrained Finite-Sum Optimization
by: Ye, Haishan, et al.
Published: (2025)
by: Ye, Haishan, et al.
Published: (2025)
Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient
by: Di, Hao, et al.
Published: (2024)
by: Di, Hao, et al.
Published: (2024)
PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction
by: He, Peilin, et al.
Published: (2025)
by: He, Peilin, et al.
Published: (2025)
Optimal High-Probability Regret for Online Convex Optimization with Two-Point Bandit Feedback
by: Ye, Haishan
Published: (2026)
by: Ye, Haishan
Published: (2026)
Explicit and Non-asymptotic Query Complexities of Rank-Based Zeroth-order Algorithm on Stochastic Smooth Functions
by: Ye, Haishan
Published: (2025)
by: Ye, Haishan
Published: (2025)
Explicit and Non-asymptotic Query Complexities of Rank-Based Zeroth-order Algorithms on Smooth Functions
by: Ye, Haishan
Published: (2025)
by: Ye, Haishan
Published: (2025)
Factor-Assisted Federated Learning for Personalized Optimization with Heterogeneous Data
by: Wang, Feifei, et al.
Published: (2023)
by: Wang, Feifei, et al.
Published: (2023)
HSTFL: A Heterogeneous Federated Learning Framework for Misaligned Spatiotemporal Forecasting
by: Cai, Shuowei, et al.
Published: (2024)
by: Cai, Shuowei, et al.
Published: (2024)
Fisher-Informed Parameterwise Aggregation for Federated Learning with Heterogeneous Data
by: Chang, Zhipeng, et al.
Published: (2026)
by: Chang, Zhipeng, et al.
Published: (2026)
Beyond Uniform Deletion: A Data Value-Weighted Framework for Certified Machine Unlearning
by: He, Lisong, et al.
Published: (2025)
by: He, Lisong, et al.
Published: (2025)
Personalized Federated Learning for Statistical Heterogeneity
by: Firdaus, Muhammad, et al.
Published: (2024)
by: Firdaus, Muhammad, et al.
Published: (2024)
Personalized Federated Learning on Heterogeneous and Long-Tailed Data via Expert Collaborative Learning
by: Lv, Fengling, et al.
Published: (2024)
by: Lv, Fengling, et al.
Published: (2024)
Heterogeneous Federated Learning via Personalized Generative Networks
by: Taghiyarrenani, Zahra, et al.
Published: (2023)
by: Taghiyarrenani, Zahra, et al.
Published: (2023)
FLASH: Federated Learning Across Simultaneous Heterogeneities
by: Chang, Xiangyu, et al.
Published: (2024)
by: Chang, Xiangyu, et al.
Published: (2024)
Learn What You Need in Personalized Federated Learning
by: Lv, Kexin, et al.
Published: (2024)
by: Lv, Kexin, et al.
Published: (2024)
Dynamic Clustering for Personalized Federated Learning on Heterogeneous Edge Devices
by: Liu, Heting, et al.
Published: (2025)
by: Liu, Heting, et al.
Published: (2025)
Personalized One-shot Federated Graph Learning for Heterogeneous Clients
by: Yan, Guochen, et al.
Published: (2024)
by: Yan, Guochen, et al.
Published: (2024)
Heterogeneity-Aware Personalized Federated Learning for Industrial Predictive Analytics
by: Hu, Yuhan, et al.
Published: (2026)
by: Hu, Yuhan, et al.
Published: (2026)
Privacy-Preserving Personalized Federated Learning for Distributed Photovoltaic Disaggregation under Statistical Heterogeneity
by: Chen, Xiaolu, et al.
Published: (2025)
by: Chen, Xiaolu, et al.
Published: (2025)
Graph Federated Learning for Personalized Privacy Recommendation
by: Na, Ce, et al.
Published: (2025)
by: Na, Ce, et al.
Published: (2025)
Tackling Data Heterogeneity in Federated Time Series Forecasting
by: Yuan, Wei, et al.
Published: (2024)
by: Yuan, Wei, et al.
Published: (2024)
On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness
by: Zhu, Shengkun, et al.
Published: (2024)
by: Zhu, Shengkun, et al.
Published: (2024)
Hypernetworks for Model-Heterogeneous Personalized Federated Learning
by: Zhang, Chen, et al.
Published: (2025)
by: Zhang, Chen, et al.
Published: (2025)
Federated Reinforcement Learning with Constraint Heterogeneity
by: Jin, Hao, et al.
Published: (2024)
by: Jin, Hao, et al.
Published: (2024)
pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated Learning
by: Yi, Liping, et al.
Published: (2024)
by: Yi, Liping, et al.
Published: (2024)
FedThief: Harming Others to Benefit Oneself in Self-Centered Federated Learning
by: Zhang, Xiangyu, et al.
Published: (2025)
by: Zhang, Xiangyu, et al.
Published: (2025)
CO-PFL: Contribution-Oriented Personalized Federated Learning for Heterogeneous Networks
by: Xing, Ke, et al.
Published: (2025)
by: Xing, Ke, et al.
Published: (2025)
A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical Datasets
by: Tavakoli, Fatemeh, et al.
Published: (2023)
by: Tavakoli, Fatemeh, et al.
Published: (2023)
Personalized Federated Dictionary Learning for Modeling Heterogeneity in Multi-site fMRI Data
by: Zhang, Yipu, et al.
Published: (2025)
by: Zhang, Yipu, et al.
Published: (2025)
Probabilistic Federated Learning on Uncertain and Heterogeneous Data with Model Personalization
by: Rahman, Ratun, et al.
Published: (2026)
by: Rahman, Ratun, et al.
Published: (2026)
Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning
by: Li, Zhilong, et al.
Published: (2024)
by: Li, Zhilong, et al.
Published: (2024)
Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data
by: Rokvic, Ljubomir, et al.
Published: (2025)
by: Rokvic, Ljubomir, et al.
Published: (2025)
Fairness May Backfire: When Leveling-Down Occurs in Fair Machine Learning
by: Yang, Yi, et al.
Published: (2026)
by: Yang, Yi, et al.
Published: (2026)
Harmonizing Generalization and Personalization in Federated Prompt Learning
by: Cui, Tianyu, et al.
Published: (2024)
by: Cui, Tianyu, et al.
Published: (2024)
ESSAM: A Novel Competitive Evolution Strategies Approach to Reinforcement Learning for Memory Efficient LLMs Fine-Tuning
by: Sun, Zhishen, et al.
Published: (2026)
by: Sun, Zhishen, et al.
Published: (2026)
Causal and Federated Multimodal Learning for Cardiovascular Risk Prediction under Heterogeneous Populations
by: Kaushik, Rohit, et al.
Published: (2026)
by: Kaushik, Rohit, et al.
Published: (2026)
Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning
by: Ye, Rongguang, et al.
Published: (2025)
by: Ye, Rongguang, et al.
Published: (2025)
FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts
by: Mei, Hanzi, et al.
Published: (2024)
by: Mei, Hanzi, et al.
Published: (2024)
pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning
by: Yi, Liping, et al.
Published: (2024)
by: Yi, Liping, et al.
Published: (2024)
FedHiP: Heterogeneity-Invariant Personalized Federated Learning Through Closed-Form Solutions
by: Tang, Jianheng, et al.
Published: (2025)
by: Tang, Jianheng, et al.
Published: (2025)
Similar Items
-
An Enhanced Zeroth-Order Stochastic Frank-Wolfe Framework for Constrained Finite-Sum Optimization
by: Ye, Haishan, et al.
Published: (2025) -
Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient
by: Di, Hao, et al.
Published: (2024) -
PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction
by: He, Peilin, et al.
Published: (2025) -
Optimal High-Probability Regret for Online Convex Optimization with Two-Point Bandit Feedback
by: Ye, Haishan
Published: (2026) -
Explicit and Non-asymptotic Query Complexities of Rank-Based Zeroth-order Algorithm on Stochastic Smooth Functions
by: Ye, Haishan
Published: (2025)