Saved in:
| Main Authors: | Seo, Jungwon, Kim, Minhoe, Rong, Chunming |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.01070 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation
by: Seo, Jungwon, et al.
Published: (2025)
by: Seo, Jungwon, et al.
Published: (2025)
Understanding Federated Learning from IID to Non-IID dataset: An Experimental Study
by: Seo, Jungwon, et al.
Published: (2025)
by: Seo, Jungwon, et al.
Published: (2025)
Federated Inference: Toward Privacy-Preserving Collaborative and Incentivized Model Serving
by: Seo, Jungwon, et al.
Published: (2026)
by: Seo, Jungwon, et al.
Published: (2026)
CATCHFed: Efficient Unlabeled Data Utilization for Semi-Supervised Federated Learning in Limited Labels Environments
by: Park, Byoungjun, et al.
Published: (2025)
by: Park, Byoungjun, et al.
Published: (2025)
FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering
by: Guo, Yongxin, et al.
Published: (2023)
by: Guo, Yongxin, et al.
Published: (2023)
Fed-ADE: Adaptive Learning Rate for Federated Post-adaptation under Distribution Shift
by: Park, Heewon, et al.
Published: (2026)
by: Park, Heewon, et al.
Published: (2026)
FedOUI: OUI-Guided Client Weighting for Federated Aggregation
by: Fernández-Hernández, Alberto, et al.
Published: (2026)
by: Fernández-Hernández, Alberto, et al.
Published: (2026)
FedCAP: Robust Federated Learning via Customized Aggregation and Personalization
by: Li, Youpeng, et al.
Published: (2024)
by: Li, Youpeng, et al.
Published: (2024)
FedIA: Towards Domain-Robust Aggregation in Federated Graph Learning
by: Zhou, Zhanting, et al.
Published: (2025)
by: Zhou, Zhanting, et al.
Published: (2025)
FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors
by: Shi, Changlong, et al.
Published: (2025)
by: Shi, Changlong, et al.
Published: (2025)
Byzantine-Robust Federated Learning with Learnable Aggregation Weights
by: Parsa, Javad, et al.
Published: (2025)
by: Parsa, Javad, et al.
Published: (2025)
FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweighting
by: Ma, Jeffrey, et al.
Published: (2024)
by: Ma, Jeffrey, et al.
Published: (2024)
Shift Aggregate Extract Networks
by: Orsini, Francesco, et al.
Published: (2017)
by: Orsini, Francesco, et al.
Published: (2017)
FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning
by: Kritharakis, Emmanouil, et al.
Published: (2025)
by: Kritharakis, Emmanouil, et al.
Published: (2025)
FedPID: An Aggregation Method for Federated Learning
by: Mächler, Leon, et al.
Published: (2024)
by: Mächler, Leon, et al.
Published: (2024)
Robust Uncertainty Estimation under Distribution Shift via Difference Reconstruction
by: Xu, Xinran, et al.
Published: (2026)
by: Xu, Xinran, et al.
Published: (2026)
Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shifts
by: Najafi, Amir, et al.
Published: (2024)
by: Najafi, Amir, et al.
Published: (2024)
GeoReg: Weight-Constrained Few-Shot Regression for Socio-Economic Estimation using LLM
by: Ahn, Kyeongjin, et al.
Published: (2025)
by: Ahn, Kyeongjin, et al.
Published: (2025)
Performative Reinforcement Learning in Gradually Shifting Environments
by: Rank, Ben, et al.
Published: (2024)
by: Rank, Ben, et al.
Published: (2024)
FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation
by: Hu, Ming, et al.
Published: (2022)
by: Hu, Ming, et al.
Published: (2022)
Quantum Key Distribution Secured Federated Learning for Channel Estimation and Radar Spectrum Sensing in 6G Networks
by: Catak, Ferhat Ozgur, et al.
Published: (2026)
by: Catak, Ferhat Ozgur, et al.
Published: (2026)
FedSCAM (Federated Sharpness-Aware Minimization with Clustered Aggregation and Modulation): Scam-resistant SAM for Robust Federated Optimization in Heterogeneous Environments
by: Rahil, Sameer, et al.
Published: (2025)
by: Rahil, Sameer, et al.
Published: (2025)
Weight Clipping for Robust Conformal Inference under Unbounded Covariate Shifts
by: Wang, James, et al.
Published: (2026)
by: Wang, James, et al.
Published: (2026)
FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning
by: Campos, Enrique Mármol, et al.
Published: (2024)
by: Campos, Enrique Mármol, et al.
Published: (2024)
FedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching Generation
by: Wang, Ruiyang, et al.
Published: (2026)
by: Wang, Ruiyang, et al.
Published: (2026)
MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts
by: Lee, Jihye, et al.
Published: (2025)
by: Lee, Jihye, et al.
Published: (2025)
Mitigating Domain Shift in Federated Learning via Intra- and Inter-Domain Prototypes
by: Le, Huy Q., et al.
Published: (2025)
by: Le, Huy Q., et al.
Published: (2025)
FedAgg: Adaptive Federated Learning with Aggregated Gradients
by: Yuan, Wenhao, et al.
Published: (2023)
by: Yuan, Wenhao, et al.
Published: (2023)
Linearly Constrained Weights: Reducing Activation Shift for Faster Training of Neural Networks
by: Kutsuna, Takuro
Published: (2024)
by: Kutsuna, Takuro
Published: (2024)
Over-the-Air Federated Learning via Weighted Aggregation
by: Azimi-Abarghouyi, Seyed Mohammad, et al.
Published: (2024)
by: Azimi-Abarghouyi, Seyed Mohammad, et al.
Published: (2024)
FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation
by: Peng, Zihao, et al.
Published: (2025)
by: Peng, Zihao, et al.
Published: (2025)
Understand the Effect of Importance Weighting in Deep Learning on Dataset Shift
by: Vo, Thien Nhan
Published: (2025)
by: Vo, Thien Nhan
Published: (2025)
Fed-SE: Federated Self-Evolution for Privacy-Constrained Multi-Environment LLM Agents
by: Chen, Xiang, et al.
Published: (2025)
by: Chen, Xiang, et al.
Published: (2025)
FedScalar: Federated Learning with Scalar Communication for Bandwidth-Constrained Networks
by: Rostami, M., et al.
Published: (2024)
by: Rostami, M., 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)
FedAH: Aggregated Head for Personalized Federated Learning
by: Zhou, Pengzhan, et al.
Published: (2024)
by: Zhou, Pengzhan, et al.
Published: (2024)
TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments
by: Lee, Gyuejeong, et al.
Published: (2025)
by: Lee, Gyuejeong, et al.
Published: (2025)
FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated Learning
by: Jiang, Zhonghua, et al.
Published: (2024)
by: Jiang, Zhonghua, et al.
Published: (2024)
FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures
by: Wang, Jiacheng, et al.
Published: (2025)
by: Wang, Jiacheng, et al.
Published: (2025)
Hybrid-Regularized Magnitude Pruning for Robust Federated Learning under Covariate Shift
by: Goksu, Ozgu, et al.
Published: (2024)
by: Goksu, Ozgu, et al.
Published: (2024)
Similar Items
-
GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation
by: Seo, Jungwon, et al.
Published: (2025) -
Understanding Federated Learning from IID to Non-IID dataset: An Experimental Study
by: Seo, Jungwon, et al.
Published: (2025) -
Federated Inference: Toward Privacy-Preserving Collaborative and Incentivized Model Serving
by: Seo, Jungwon, et al.
Published: (2026) -
CATCHFed: Efficient Unlabeled Data Utilization for Semi-Supervised Federated Learning in Limited Labels Environments
by: Park, Byoungjun, et al.
Published: (2025) -
FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering
by: Guo, Yongxin, et al.
Published: (2023)