Uncertainty-Aware Explainable Federated Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Zhang, Yanci, Yu, Han |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Fairness-Aware Job Scheduling for Multi-Job Federated Learning
by: Shi, Yuxin, et al.
Published: (2024)
by: Shi, Yuxin, et al.
Published: (2024)
Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learning
by: Gao, Zhidong, et al.
Published: (2024)
by: Gao, Zhidong, et al.
Published: (2024)
TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning
by: Hu, Gangqiang, et al.
Published: (2024)
by: Hu, Gangqiang, et al.
Published: (2024)
Gradient Correction in Federated Learning with Adaptive Optimization
by: Chen, Evan, et al.
Published: (2025)
by: Chen, Evan, et al.
Published: (2025)
FedImpro: Measuring and Improving Client Update in Federated Learning
by: Tang, Zhenheng, et al.
Published: (2024)
by: Tang, Zhenheng, et al.
Published: (2024)
Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning
by: Gao, Zhidong, et al.
Published: (2024)
by: Gao, Zhidong, et al.
Published: (2024)
A Fast and Flat Federated Learning Method via Weighted Momentum and Sharpness-Aware Minimization
by: Li, Tianle, et al.
Published: (2025)
by: Li, Tianle, et al.
Published: (2025)
SemiDFL: A Semi-Supervised Paradigm for Decentralized Federated Learning
by: Liu, Xinyang, et al.
Published: (2024)
by: Liu, Xinyang, et al.
Published: (2024)
CA-AFP: Cluster-Aware Adaptive Federated Pruning
by: Jha, Om Govind, et al.
Published: (2026)
by: Jha, Om Govind, et al.
Published: (2026)
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)
Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning
by: Wang, Zijian, et al.
Published: (2025)
by: Wang, Zijian, et al.
Published: (2025)
Influence-oriented Personalized Federated Learning
by: Tan, Yue, et al.
Published: (2024)
by: Tan, Yue, et al.
Published: (2024)
A Resource-Adaptive Approach for Federated Learning under Resource-Constrained Environments
by: Zhang, Ruirui, et al.
Published: (2024)
by: Zhang, Ruirui, et al.
Published: (2024)
AugFL: Augmenting Federated Learning with Pretrained Models
by: Yue, Sheng, et al.
Published: (2025)
by: Yue, Sheng, et al.
Published: (2025)
FedMT: Federated Learning with Mixed-type Labels
by: Zhang, Qiong, et al.
Published: (2022)
by: Zhang, Qiong, et al.
Published: (2022)
DFML: Decentralized Federated Mutual Learning
by: Khalil, Yasser H., et al.
Published: (2024)
by: Khalil, Yasser H., et al.
Published: (2024)
Federated Graph Learning with Structure Proxy Alignment
by: Fu, Xingbo, et al.
Published: (2024)
by: Fu, Xingbo, et al.
Published: (2024)
Learn How to Query from Unlabeled Data Streams in Federated Learning
by: Sun, Yuchang, et al.
Published: (2024)
by: Sun, Yuchang, et al.
Published: (2024)
Accelerating Privacy-Preserving Federated Learning in Large-Scale LEO Satellite Systems
by: Guo, Binquan, et al.
Published: (2025)
by: Guo, Binquan, et al.
Published: (2025)
Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments
by: Sun, Pengcheng, et al.
Published: (2024)
by: Sun, Pengcheng, et al.
Published: (2024)
Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout
by: Liu, Ji, et al.
Published: (2025)
by: Liu, Ji, et al.
Published: (2025)
Federated Multi-Objective Learning
by: Yang, Haibo, et al.
Published: (2023)
by: Yang, Haibo, et al.
Published: (2023)
Federated Learning with Flexible Architectures
by: Park, Jong-Ik, et al.
Published: (2024)
by: Park, Jong-Ik, 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)
LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data
by: Zhang, Yuxin, et al.
Published: (2025)
by: Zhang, Yuxin, et al.
Published: (2025)
From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges
by: Li, Qiongxiu, et al.
Published: (2025)
by: Li, Qiongxiu, et al.
Published: (2025)
On Using Large-Batches in Federated Learning
by: Tyagi, Sahil
Published: (2025)
by: Tyagi, Sahil
Published: (2025)
Efficient Client Selection in Federated Learning
by: Marfo, William, et al.
Published: (2025)
by: Marfo, William, et al.
Published: (2025)
Tackling Selfish Clients in Federated Learning
by: Augello, Andrea, et al.
Published: (2024)
by: Augello, Andrea, et al.
Published: (2024)
Federated Learning with Limited Node Labels
by: Tang, Bisheng, et al.
Published: (2024)
by: Tang, Bisheng, et al.
Published: (2024)
Variational Bayes for Federated Continual Learning
by: Yao, Dezhong, et al.
Published: (2024)
by: Yao, Dezhong, et al.
Published: (2024)
FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning
by: Wang, Zihui, et al.
Published: (2024)
by: Wang, Zihui, et al.
Published: (2024)
EASTER: Embedding Aggregation-based Heterogeneous Models Training in Vertical Federated Learning
by: Wang, Shuo, et al.
Published: (2023)
by: Wang, Shuo, et al.
Published: (2023)
FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data
by: Zhang, Yuxin, et al.
Published: (2024)
by: Zhang, Yuxin, et al.
Published: (2024)
Towards Straggler-Resilient Split Federated Learning: An Unbalanced Update Approach
by: Liang, Dandan, et al.
Published: (2025)
by: Liang, Dandan, et al.
Published: (2025)
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)
FedFa: A Fully Asynchronous Training Paradigm for Federated Learning
by: Xu, Haotian, et al.
Published: (2024)
by: Xu, Haotian, et al.
Published: (2024)
Multimodal Federated Learning with Missing Modality via Prototype Mask and Contrast
by: Bao, Guangyin, et al.
Published: (2023)
by: Bao, Guangyin, et al.
Published: (2023)
Efficient Federated Learning with Timely Update Dissemination
by: Jia, Juncheng, et al.
Published: (2025)
by: Jia, Juncheng, et al.
Published: (2025)
Similar Items
-
Fairness-Aware Job Scheduling for Multi-Job Federated Learning
by: Shi, Yuxin, et al.
Published: (2024) -
Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learning
by: Gao, Zhidong, et al.
Published: (2024) -
TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning
by: Hu, Gangqiang, et al.
Published: (2024) -
Gradient Correction in Federated Learning with Adaptive Optimization
by: Chen, Evan, et al.
Published: (2025) -
FedImpro: Measuring and Improving Client Update in Federated Learning
by: Tang, Zhenheng, et al.
Published: (2024)