Incentive-Compatible Federated Learning with Stackelberg Game Modeling
Fuente:
arXiv
Guardado en:
| Autores principales: | Javaherian, Simin, Turney, Bryce, Chen, Li, Tzeng, Nian-Feng |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering
por: Islam, Md Sirajul, et al.
Publicado: (2024)
por: Islam, Md Sirajul, et al.
Publicado: (2024)
FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering
por: Islam, Md Sirajul, et al.
Publicado: (2024)
por: Islam, Md Sirajul, 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)
SEAFL: Enhancing Efficiency in Semi-Asynchronous Federated Learning through Adaptive Aggregation and Selective Training
por: Islam, Md Sirajul, et al.
Publicado: (2025)
por: Islam, Md Sirajul, et al.
Publicado: (2025)
Blockchain-based Framework for Scalable and Incentivized Federated Learning
por: Wu, Bijun, et al.
Publicado: (2025)
por: Wu, Bijun, et al.
Publicado: (2025)
Incentive-Based Federated Learning: Architectural Elements and Future Directions
por: Kaluannakkage, Chanuka A. S. Hewa, et al.
Publicado: (2025)
por: Kaluannakkage, Chanuka A. S. Hewa, et al.
Publicado: (2025)
FEDBUD: Joint Incentive and Privacy Optimization for Resource-Constrained Federated Learning
por: Liu, Tao, et al.
Publicado: (2026)
por: Liu, Tao, et al.
Publicado: (2026)
FedACT: Concurrent Federated Intelligence across Heterogeneous Data Sources
por: Islam, Md Sirajul, et al.
Publicado: (2026)
por: Islam, Md Sirajul, et al.
Publicado: (2026)
Incentivizing Permissionless Distributed Learning of LLMs
por: Lidin, Joel, et al.
Publicado: (2025)
por: Lidin, Joel, et al.
Publicado: (2025)
An All-Reduce Compatible Top-K Compressor for Communication-Efficient Distributed Learning
por: Chen, Chuyan, et al.
Publicado: (2025)
por: Chen, Chuyan, et al.
Publicado: (2025)
Hypernetworks for Model-Heterogeneous Personalized Federated Learning
por: Zhang, Chen, et al.
Publicado: (2025)
por: Zhang, Chen, et al.
Publicado: (2025)
Incentivizing High-quality Participation From Federated Learning Agents
por: Pang, Jinlong, et al.
Publicado: (2025)
por: Pang, Jinlong, et al.
Publicado: (2025)
Design of Two-Level Incentive Mechanisms for Hierarchical Federated Learning
por: Chu, Shunfeng, et al.
Publicado: (2023)
por: Chu, Shunfeng, et al.
Publicado: (2023)
Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations
por: Shao, Yumeng, et al.
Publicado: (2024)
por: Shao, Yumeng, et al.
Publicado: (2024)
EMO: Edge Model Overlays to Scale Model Size in Federated Learning
por: Wu, Di, et al.
Publicado: (2025)
por: Wu, Di, et al.
Publicado: (2025)
Empowering Federated Learning for Massive Models with NVIDIA FLARE
por: Roth, Holger R., et al.
Publicado: (2024)
por: Roth, Holger R., et al.
Publicado: (2024)
Federated Graph Learning with Graphless Clients
por: Fu, Xingbo, et al.
Publicado: (2024)
por: Fu, Xingbo, et al.
Publicado: (2024)
Sketched Gaussian Mechanism for Private Federated Learning
por: Li, Qiaobo, et al.
Publicado: (2025)
por: Li, Qiaobo, et al.
Publicado: (2025)
Federated Model Heterogeneous Matryoshka Representation Learning
por: Yi, Liping, et al.
Publicado: (2024)
por: Yi, Liping, et al.
Publicado: (2024)
Federated Learning on Stochastic Neural Networks
por: Tang, Jingqiao, et al.
Publicado: (2025)
por: Tang, Jingqiao, et al.
Publicado: (2025)
Partial Federated Learning
por: Feng, Tiantian, et al.
Publicado: (2024)
por: Feng, Tiantian, et al.
Publicado: (2024)
MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes
por: Li, Xin-Chun, et al.
Publicado: (2024)
por: Li, Xin-Chun, et al.
Publicado: (2024)
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
por: Ji, Shaoxiong, et al.
Publicado: (2021)
por: Ji, Shaoxiong, et al.
Publicado: (2021)
Knowledge-Driven Federated Graph Learning on Model Heterogeneity
por: Wu, Zhengyu, et al.
Publicado: (2025)
por: Wu, Zhengyu, et al.
Publicado: (2025)
GPT-FL: Generative Pre-trained Model-Assisted Federated Learning
por: Zhang, Tuo, et al.
Publicado: (2023)
por: Zhang, Tuo, et al.
Publicado: (2023)
Decentralized Federated Learning with Model Caching on Mobile Agents
por: Wang, Xiaoyu, et al.
Publicado: (2024)
por: Wang, Xiaoyu, et al.
Publicado: (2024)
Ilargi: a GPU Compatible Factorized ML Model Training Framework
por: Sun, Wenbo, et al.
Publicado: (2025)
por: Sun, Wenbo, et al.
Publicado: (2025)
Robust Federated Learning against Model Perturbation in Edge Networks
por: Jin, Dongzi, et al.
Publicado: (2025)
por: Jin, Dongzi, et al.
Publicado: (2025)
Accelerating Wireless Distributed Learning via Hybrid Split and Federated Learning Optimization
por: Guo, Kun, et al.
Publicado: (2025)
por: Guo, Kun, et al.
Publicado: (2025)
Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting
por: Tian, Chunlin, et al.
Publicado: (2024)
por: Tian, Chunlin, et al.
Publicado: (2024)
FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout
por: Feng, Zhiying, et al.
Publicado: (2023)
por: Feng, Zhiying, et al.
Publicado: (2023)
Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients
por: Ma, Mengmeng, et al.
Publicado: (2024)
por: Ma, Mengmeng, et al.
Publicado: (2024)
Rashomon Sets and Model Multiplicity in Federated Learning
por: Heilmann, Xenia, et al.
Publicado: (2026)
por: Heilmann, Xenia, et al.
Publicado: (2026)
Efficient Model Compression for Hierarchical Federated Learning
por: Zhu, Xi, et al.
Publicado: (2024)
por: Zhu, Xi, et al.
Publicado: (2024)
Empowering Data Mesh with Federated Learning
por: Li, Haoyuan, et al.
Publicado: (2024)
por: Li, Haoyuan, et al.
Publicado: (2024)
Towards Client Driven Federated Learning
por: Li, Songze, et al.
Publicado: (2024)
por: Li, Songze, 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)
Flexible Personalized Split Federated Learning for On-Device Fine-Tuning of Foundation Models
por: Yuan, Tianjun, et al.
Publicado: (2025)
por: Yuan, Tianjun, et al.
Publicado: (2025)
Vertical Federated Learning: Challenges, Methodologies and Experiments
por: Wei, Kang, et al.
Publicado: (2022)
por: Wei, Kang, et al.
Publicado: (2022)
Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning
por: Fu, Xingbo, et al.
Publicado: (2024)
por: Fu, Xingbo, et al.
Publicado: (2024)
Ejemplares similares
-
FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering
por: Islam, Md Sirajul, et al.
Publicado: (2024) -
FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering
por: Islam, Md Sirajul, et al.
Publicado: (2024) -
FedFair^3: Unlocking Threefold Fairness in Federated Learning
por: Javaherian, Simin, et al.
Publicado: (2024) -
SEAFL: Enhancing Efficiency in Semi-Asynchronous Federated Learning through Adaptive Aggregation and Selective Training
por: Islam, Md Sirajul, et al.
Publicado: (2025) -
Blockchain-based Framework for Scalable and Incentivized Federated Learning
por: Wu, Bijun, et al.
Publicado: (2025)