Guardado en:
| Autores principales: | Seo, Jungwon, Kim, Minhoe, Rong, Chunming |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2402.01070 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation
por: Seo, Jungwon, et al.
Publicado: (2025)
por: Seo, Jungwon, et al.
Publicado: (2025)
Understanding Federated Learning from IID to Non-IID dataset: An Experimental Study
por: Seo, Jungwon, et al.
Publicado: (2025)
por: Seo, Jungwon, et al.
Publicado: (2025)
Federated Inference: Toward Privacy-Preserving Collaborative and Incentivized Model Serving
por: Seo, Jungwon, et al.
Publicado: (2026)
por: Seo, Jungwon, et al.
Publicado: (2026)
CATCHFed: Efficient Unlabeled Data Utilization for Semi-Supervised Federated Learning in Limited Labels Environments
por: Park, Byoungjun, et al.
Publicado: (2025)
por: Park, Byoungjun, et al.
Publicado: (2025)
FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering
por: Guo, Yongxin, et al.
Publicado: (2023)
por: Guo, Yongxin, et al.
Publicado: (2023)
Fed-ADE: Adaptive Learning Rate for Federated Post-adaptation under Distribution Shift
por: Park, Heewon, et al.
Publicado: (2026)
por: Park, Heewon, et al.
Publicado: (2026)
FedOUI: OUI-Guided Client Weighting for Federated Aggregation
por: Fernández-Hernández, Alberto, et al.
Publicado: (2026)
por: Fernández-Hernández, Alberto, et al.
Publicado: (2026)
FedCAP: Robust Federated Learning via Customized Aggregation and Personalization
por: Li, Youpeng, et al.
Publicado: (2024)
por: Li, Youpeng, et al.
Publicado: (2024)
FedIA: Towards Domain-Robust Aggregation in Federated Graph Learning
por: Zhou, Zhanting, et al.
Publicado: (2025)
por: Zhou, Zhanting, et al.
Publicado: (2025)
FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors
por: Shi, Changlong, et al.
Publicado: (2025)
por: Shi, Changlong, et al.
Publicado: (2025)
Byzantine-Robust Federated Learning with Learnable Aggregation Weights
por: Parsa, Javad, et al.
Publicado: (2025)
por: Parsa, Javad, 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)
Shift Aggregate Extract Networks
por: Orsini, Francesco, et al.
Publicado: (2017)
por: Orsini, Francesco, et al.
Publicado: (2017)
FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning
por: Kritharakis, Emmanouil, et al.
Publicado: (2025)
por: Kritharakis, Emmanouil, et al.
Publicado: (2025)
FedPID: An Aggregation Method for Federated Learning
por: Mächler, Leon, et al.
Publicado: (2024)
por: Mächler, Leon, et al.
Publicado: (2024)
Robust Uncertainty Estimation under Distribution Shift via Difference Reconstruction
por: Xu, Xinran, et al.
Publicado: (2026)
por: Xu, Xinran, et al.
Publicado: (2026)
Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shifts
por: Najafi, Amir, et al.
Publicado: (2024)
por: Najafi, Amir, et al.
Publicado: (2024)
GeoReg: Weight-Constrained Few-Shot Regression for Socio-Economic Estimation using LLM
por: Ahn, Kyeongjin, et al.
Publicado: (2025)
por: Ahn, Kyeongjin, et al.
Publicado: (2025)
Performative Reinforcement Learning in Gradually Shifting Environments
por: Rank, Ben, et al.
Publicado: (2024)
por: Rank, Ben, et al.
Publicado: (2024)
FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation
por: Hu, Ming, et al.
Publicado: (2022)
por: Hu, Ming, et al.
Publicado: (2022)
Quantum Key Distribution Secured Federated Learning for Channel Estimation and Radar Spectrum Sensing in 6G Networks
por: Catak, Ferhat Ozgur, et al.
Publicado: (2026)
por: Catak, Ferhat Ozgur, et al.
Publicado: (2026)
FedSCAM (Federated Sharpness-Aware Minimization with Clustered Aggregation and Modulation): Scam-resistant SAM for Robust Federated Optimization in Heterogeneous Environments
por: Rahil, Sameer, et al.
Publicado: (2025)
por: Rahil, Sameer, et al.
Publicado: (2025)
Weight Clipping for Robust Conformal Inference under Unbounded Covariate Shifts
por: Wang, James, et al.
Publicado: (2026)
por: Wang, James, et al.
Publicado: (2026)
FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning
por: Campos, Enrique Mármol, et al.
Publicado: (2024)
por: Campos, Enrique Mármol, et al.
Publicado: (2024)
FedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching Generation
por: Wang, Ruiyang, et al.
Publicado: (2026)
por: Wang, Ruiyang, et al.
Publicado: (2026)
MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts
por: Lee, Jihye, et al.
Publicado: (2025)
por: Lee, Jihye, et al.
Publicado: (2025)
Mitigating Domain Shift in Federated Learning via Intra- and Inter-Domain Prototypes
por: Le, Huy Q., et al.
Publicado: (2025)
por: Le, Huy Q., 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)
Linearly Constrained Weights: Reducing Activation Shift for Faster Training of Neural Networks
por: Kutsuna, Takuro
Publicado: (2024)
por: Kutsuna, Takuro
Publicado: (2024)
Over-the-Air Federated Learning via Weighted Aggregation
por: Azimi-Abarghouyi, Seyed Mohammad, et al.
Publicado: (2024)
por: Azimi-Abarghouyi, Seyed Mohammad, et al.
Publicado: (2024)
FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation
por: Peng, Zihao, et al.
Publicado: (2025)
por: Peng, Zihao, et al.
Publicado: (2025)
Understand the Effect of Importance Weighting in Deep Learning on Dataset Shift
por: Vo, Thien Nhan
Publicado: (2025)
por: Vo, Thien Nhan
Publicado: (2025)
Fed-SE: Federated Self-Evolution for Privacy-Constrained Multi-Environment LLM Agents
por: Chen, Xiang, et al.
Publicado: (2025)
por: Chen, Xiang, et al.
Publicado: (2025)
FedScalar: Federated Learning with Scalar Communication for Bandwidth-Constrained Networks
por: Rostami, M., et al.
Publicado: (2024)
por: Rostami, M., et al.
Publicado: (2024)
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)
FedAH: Aggregated Head for Personalized Federated Learning
por: Zhou, Pengzhan, et al.
Publicado: (2024)
por: Zhou, Pengzhan, et al.
Publicado: (2024)
TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments
por: Lee, Gyuejeong, et al.
Publicado: (2025)
por: Lee, Gyuejeong, et al.
Publicado: (2025)
FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated Learning
por: Jiang, Zhonghua, et al.
Publicado: (2024)
por: Jiang, Zhonghua, et al.
Publicado: (2024)
FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures
por: Wang, Jiacheng, et al.
Publicado: (2025)
por: Wang, Jiacheng, et al.
Publicado: (2025)
Hybrid-Regularized Magnitude Pruning for Robust Federated Learning under Covariate Shift
por: Goksu, Ozgu, et al.
Publicado: (2024)
por: Goksu, Ozgu, et al.
Publicado: (2024)
Ejemplares similares
-
GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation
por: Seo, Jungwon, et al.
Publicado: (2025) -
Understanding Federated Learning from IID to Non-IID dataset: An Experimental Study
por: Seo, Jungwon, et al.
Publicado: (2025) -
Federated Inference: Toward Privacy-Preserving Collaborative and Incentivized Model Serving
por: Seo, Jungwon, et al.
Publicado: (2026) -
CATCHFed: Efficient Unlabeled Data Utilization for Semi-Supervised Federated Learning in Limited Labels Environments
por: Park, Byoungjun, et al.
Publicado: (2025) -
FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering
por: Guo, Yongxin, et al.
Publicado: (2023)