FedGA: A Fair Federated Learning Framework Based on the Gini Coefficient
Fuente:
arXiv
Guardado en:
| Autor principal: | Liu, ShanBin |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation
por: Ma, Qianpiao, et al.
Publicado: (2025)
por: Ma, Qianpiao, et al.
Publicado: (2025)
FedTilt: Towards Multi-Level Fairness-Preserving and Robust Federated Learning
por: Zhang, Binghui, et al.
Publicado: (2025)
por: Zhang, Binghui, et al.
Publicado: (2025)
FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweighting
por: Ma, Jeffrey, et al.
Publicado: (2024)
por: Ma, Jeffrey, et al.
Publicado: (2024)
FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems
por: Li, Li, et al.
Publicado: (2021)
por: Li, Li, et al.
Publicado: (2021)
GradualDiff-Fed: A Federated Learning Specialized Framework for Large Language Model
por: Faiyaz, Amir, et al.
Publicado: (2025)
por: Faiyaz, Amir, et al.
Publicado: (2025)
FedHB: Hierarchical Bayesian Federated Learning
por: Kim, Minyoung, et al.
Publicado: (2023)
por: Kim, Minyoung, et al.
Publicado: (2023)
FedFetch: Faster Federated Learning with Adaptive Downstream Prefetching
por: Yan, Qifan, et al.
Publicado: (2025)
por: Yan, Qifan, et al.
Publicado: (2025)
FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning
por: Li, Daoyuan, et al.
Publicado: (2025)
por: Li, Daoyuan, et al.
Publicado: (2025)
FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning
por: Li, Jian, et al.
Publicado: (2024)
por: Li, Jian, et al.
Publicado: (2024)
FedOptima: Optimizing Resource Utilization in Federated Learning
por: Zhang, Zihan, et al.
Publicado: (2025)
por: Zhang, Zihan, et al.
Publicado: (2025)
FedRIR: Rethinking Information Representation in Federated Learning
por: Huang, Yongqiang, et al.
Publicado: (2025)
por: Huang, Yongqiang, et al.
Publicado: (2025)
FedAgg: Adaptive Federated Learning with Aggregated Gradients
por: Yuan, Wenhao, et al.
Publicado: (2023)
por: Yuan, Wenhao, et al.
Publicado: (2023)
FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization
por: Ning, Zhiyuan, et al.
Publicado: (2024)
por: Ning, Zhiyuan, et al.
Publicado: (2024)
FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning
por: Wang, Zihui, et al.
Publicado: (2024)
por: Wang, Zihui, et al.
Publicado: (2024)
CurvFed: Curvature-Aligned Federated Learning for Fairness without Demographics
por: Sharma, Harshit, et al.
Publicado: (2024)
por: Sharma, Harshit, et al.
Publicado: (2024)
FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning
por: Zhou, Liuzhi, et al.
Publicado: (2024)
por: Zhou, Liuzhi, et al.
Publicado: (2024)
FedFair^3: Unlocking Threefold Fairness in Federated Learning
por: Javaherian, Simin, et al.
Publicado: (2024)
por: Javaherian, Simin, et al.
Publicado: (2024)
FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure
por: Duan, Moming, et al.
Publicado: (2020)
por: Duan, Moming, et al.
Publicado: (2020)
FedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Quality
por: Yan, Biwei, et al.
Publicado: (2024)
por: Yan, Biwei, et al.
Publicado: (2024)
Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data
por: Chen, Yiyue, et al.
Publicado: (2025)
por: Chen, Yiyue, et al.
Publicado: (2025)
FedZero: Leveraging Renewable Excess Energy in Federated Learning
por: Wiesner, Philipp, et al.
Publicado: (2023)
por: Wiesner, Philipp, et al.
Publicado: (2023)
FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning
por: He, Jialuo, et al.
Publicado: (2024)
por: He, Jialuo, et al.
Publicado: (2024)
DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations
por: Zhu, Guogang, et al.
Publicado: (2024)
por: Zhu, Guogang, et al.
Publicado: (2024)
FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration
por: Feng, Xue, et al.
Publicado: (2025)
por: Feng, Xue, et al.
Publicado: (2025)
FedPref: Federated Learning Across Heterogeneous Multi-objective Preferences
por: Hartmann, Maria, et al.
Publicado: (2025)
por: Hartmann, Maria, et al.
Publicado: (2025)
FedBAT: Communication-Efficient Federated Learning via Learnable Binarization
por: Li, Shiwei, et al.
Publicado: (2024)
por: Li, Shiwei, et al.
Publicado: (2024)
FedQHD: Closed-Form Function-Space Federated Reinforcement Learning
por: Hou, Yuchen, et al.
Publicado: (2026)
por: Hou, Yuchen, et al.
Publicado: (2026)
FedFisher: Leveraging Fisher Information for One-Shot Federated Learning
por: Jhunjhunwala, Divyansh, et al.
Publicado: (2024)
por: Jhunjhunwala, Divyansh, et al.
Publicado: (2024)
FedRandom: Sampling Consistent and Accurate Contribution Values in Federated Learning
por: Geimer, Arno, et al.
Publicado: (2026)
por: Geimer, Arno, et al.
Publicado: (2026)
LoGoFair: Post-Processing for Local and Global Fairness in Federated Learning
por: Zhang, Li, et al.
Publicado: (2025)
por: Zhang, Li, et al.
Publicado: (2025)
FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs
por: Zhou, Zihao, et al.
Publicado: (2025)
por: Zhou, Zihao, et al.
Publicado: (2025)
pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning
por: Yi, Liping, et al.
Publicado: (2023)
por: Yi, Liping, et al.
Publicado: (2023)
FedHFT: Efficient Federated Finetuning with Heterogeneous Edge Clients
por: Ilhan, Fatih, et al.
Publicado: (2025)
por: Ilhan, Fatih, et al.
Publicado: (2025)
Fed-GAME: Personalized Federated Learning with Graph Attention Mixture-of-Experts For Time-Series Forecasting
por: Li, Yi, et al.
Publicado: (2026)
por: Li, Yi, et al.
Publicado: (2026)
FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant Clients
por: Liang, Han, et al.
Publicado: (2024)
por: Liang, Han, et al.
Publicado: (2024)
AirFed: A Federated Graph-Enhanced Multi-Agent Reinforcement Learning Framework for Multi-UAV Cooperative Mobile Edge Computing
por: Wang, Zhiyu, et al.
Publicado: (2025)
por: Wang, Zhiyu, et al.
Publicado: (2025)
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning
por: Yi, Liping, et al.
Publicado: (2023)
por: Yi, Liping, et al.
Publicado: (2023)
FedAST: Federated Asynchronous Simultaneous Training
por: Askin, Baris, et al.
Publicado: (2024)
por: Askin, Baris, et al.
Publicado: (2024)
SyncFed: Time-Aware Federated Learning through Explicit Timestamping and Synchronization
por: Gül, Baran Can, et al.
Publicado: (2025)
por: Gül, Baran Can, et al.
Publicado: (2025)
FedORGP: Guiding Heterogeneous Federated Learning with Orthogonality Regularization on Global Prototypes
por: Guo, Fucheng, et al.
Publicado: (2025)
por: Guo, Fucheng, et al.
Publicado: (2025)
Ejemplares similares
-
Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation
por: Ma, Qianpiao, et al.
Publicado: (2025) -
FedTilt: Towards Multi-Level Fairness-Preserving and Robust Federated Learning
por: Zhang, Binghui, et al.
Publicado: (2025) -
FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweighting
por: Ma, Jeffrey, et al.
Publicado: (2024) -
FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems
por: Li, Li, et al.
Publicado: (2021) -
GradualDiff-Fed: A Federated Learning Specialized Framework for Large Language Model
por: Faiyaz, Amir, et al.
Publicado: (2025)