CurvFed: Curvature-Aligned Federated Learning for Fairness without Demographics
Fuente:
arXiv
Saved in:
| Main Authors: | Sharma, Harshit, Roy, Shaily, Salekin, Asif |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning
by: Wang, Zihui, et al.
Published: (2024)
by: Wang, Zihui, et al.
Published: (2024)
FedFair^3: Unlocking Threefold Fairness in Federated Learning
by: Javaherian, Simin, et al.
Published: (2024)
by: Javaherian, Simin, et al.
Published: (2024)
FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning
by: He, Jialuo, et al.
Published: (2024)
by: He, Jialuo, et al.
Published: (2024)
FedDAG: Clustered Federated Learning via Global Data and Gradient Integration for Heterogeneous Environments
by: Pramanik, Anik, et al.
Published: (2026)
by: Pramanik, Anik, et al.
Published: (2026)
FedAH: Aggregated Head for Personalized Federated Learning
by: Zhou, Pengzhan, et al.
Published: (2024)
by: Zhou, Pengzhan, et al.
Published: (2024)
FedTLU: Federated Learning with Targeted Layer Updates
by: Park, Jong-Ik, et al.
Published: (2024)
by: Park, Jong-Ik, et al.
Published: (2024)
FedUV: Uniformity and Variance for Heterogeneous Federated Learning
by: Son, Ha Min, et al.
Published: (2024)
by: Son, Ha Min, et al.
Published: (2024)
FedMT: Federated Learning with Mixed-type Labels
by: Zhang, Qiong, et al.
Published: (2022)
by: Zhang, Qiong, et al.
Published: (2022)
FedCore: Straggler-Free Federated Learning with Distributed Coresets
by: Guo, Hongpeng, et al.
Published: (2024)
by: Guo, Hongpeng, et al.
Published: (2024)
FedImpro: Measuring and Improving Client Update in Federated Learning
by: Tang, Zhenheng, et al.
Published: (2024)
by: Tang, Zhenheng, et al.
Published: (2024)
FedGreen: Carbon-aware Federated Learning with Model Size Adaptation
by: Abbasi, Ali, et al.
Published: (2024)
by: Abbasi, Ali, et al.
Published: (2024)
FedTrans: Efficient Federated Learning via Multi-Model Transformation
by: Zhu, Yuxuan, et al.
Published: (2024)
by: Zhu, Yuxuan, et al.
Published: (2024)
FedFa: A Fully Asynchronous Training Paradigm for Federated Learning
by: Xu, Haotian, et al.
Published: (2024)
by: Xu, Haotian, et al.
Published: (2024)
FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data
by: Zhang, Yuxin, et al.
Published: (2024)
by: Zhang, Yuxin, et al.
Published: (2024)
GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation
by: Seo, Jungwon, et al.
Published: (2025)
by: Seo, Jungwon, et al.
Published: (2025)
Federated Fairness without Access to Sensitive Groups
by: Papadaki, Afroditi, et al.
Published: (2024)
by: Papadaki, Afroditi, et al.
Published: (2024)
FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing
by: Jia, Yongzhe, et al.
Published: (2024)
by: Jia, Yongzhe, et al.
Published: (2024)
Fed-Sophia: A Communication-Efficient Second-Order Federated Learning Algorithm
by: Elbakary, Ahmed, et al.
Published: (2024)
by: Elbakary, Ahmed, et al.
Published: (2024)
FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning
by: Chen, Tan, et al.
Published: (2025)
by: Chen, Tan, et al.
Published: (2025)
FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs
by: Chen, Zihan, et al.
Published: (2025)
by: Chen, Zihan, et al.
Published: (2025)
FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks
by: Lin, Zheng, et al.
Published: (2023)
by: Lin, Zheng, et al.
Published: (2023)
FedPop: Federated Population-based Hyperparameter Tuning
by: Chen, Haokun, et al.
Published: (2023)
by: Chen, Haokun, et al.
Published: (2023)
Fairness-Aware Job Scheduling for Multi-Job Federated Learning
by: Shi, Yuxin, et al.
Published: (2024)
by: Shi, Yuxin, et al.
Published: (2024)
Calibre: Towards Fair and Accurate Personalized Federated Learning with Self-Supervised Learning
by: Chen, Sijia, et al.
Published: (2024)
by: Chen, Sijia, et al.
Published: (2024)
SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework
by: Zhang, Yuxin, et al.
Published: (2024)
by: Zhang, Yuxin, et al.
Published: (2024)
FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering
by: Islam, Md Sirajul, et al.
Published: (2024)
by: Islam, Md Sirajul, et al.
Published: (2024)
FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices
by: Yao, Dezhong, et al.
Published: (2025)
by: Yao, Dezhong, et al.
Published: (2025)
FedFiTS: Fitness-Selected, Slotted Client Scheduling for Trustworthy Federated Learning in Healthcare AI
by: Kahenga, Ferdinand, et al.
Published: (2025)
by: Kahenga, Ferdinand, et al.
Published: (2025)
FedFusion: Federated Learning with Diversity- and Cluster-Aware Encoders for Robust Adaptation under Label Scarcity
by: Kahenga, Ferdinand, et al.
Published: (2025)
by: Kahenga, Ferdinand, et al.
Published: (2025)
FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data
by: Jimenez-Gutierrez, Daniel M., et al.
Published: (2026)
by: Jimenez-Gutierrez, Daniel M., et al.
Published: (2026)
FedACT: Concurrent Federated Intelligence across Heterogeneous Data Sources
by: Islam, Md Sirajul, et al.
Published: (2026)
by: Islam, Md Sirajul, et al.
Published: (2026)
FedPBS: Proximal-Balanced Scaling Federated Learning Model for Robust Personalized Training for Non-IID Data
by: AbouNassar, Eman M., et al.
Published: (2026)
by: AbouNassar, Eman M., et al.
Published: (2026)
FedASTA: Federated adaptive spatial-temporal attention for traffic flow prediction
by: Li, Kaiyuan, et al.
Published: (2024)
by: Li, Kaiyuan, et al.
Published: (2024)
FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning
by: Yoon, Taehwan, et al.
Published: (2025)
by: Yoon, Taehwan, et al.
Published: (2025)
Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs
by: Mohammadi, Samaneh, et al.
Published: (2025)
by: Mohammadi, Samaneh, et al.
Published: (2025)
CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models
by: Wu, Huiwen, et al.
Published: (2024)
by: Wu, Huiwen, et al.
Published: (2024)
FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients
by: Su, Shangchao, et al.
Published: (2023)
by: Su, Shangchao, et al.
Published: (2023)
FedFlex: Federated Learning for Diverse Netflix Recommendations
by: Lankester, Sven, et al.
Published: (2025)
by: Lankester, Sven, et al.
Published: (2025)
FedGA: A Fair Federated Learning Framework Based on the Gini Coefficient
by: Liu, ShanBin
Published: (2025)
by: Liu, ShanBin
Published: (2025)
FedTilt: Towards Multi-Level Fairness-Preserving and Robust Federated Learning
by: Zhang, Binghui, et al.
Published: (2025)
by: Zhang, Binghui, et al.
Published: (2025)
Similar Items
-
FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning
by: Wang, Zihui, et al.
Published: (2024) -
FedFair^3: Unlocking Threefold Fairness in Federated Learning
by: Javaherian, Simin, et al.
Published: (2024) -
FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning
by: He, Jialuo, et al.
Published: (2024) -
FedDAG: Clustered Federated Learning via Global Data and Gradient Integration for Heterogeneous Environments
by: Pramanik, Anik, et al.
Published: (2026) -
FedAH: Aggregated Head for Personalized Federated Learning
by: Zhou, Pengzhan, et al.
Published: (2024)