Resource-Aware Aggregation and Sparsification in Heterogeneous Ensemble Federated Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Ryum, Keumseo, Gong, Jinu, Kang, Joonhyuk |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations
by: Shao, Yumeng, et al.
Published: (2024)
by: Shao, Yumeng, et al.
Published: (2024)
Heterogeneity-Aware Cooperative Federated Edge Learning with Adaptive Computation and Communication Compression
by: Zhang, Zhenxiao, et al.
Published: (2024)
by: Zhang, Zhenxiao, 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)
Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning
by: Balivada, Nihal, et al.
Published: (2026)
by: Balivada, Nihal, et al.
Published: (2026)
A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks
by: Liu, Zhuocheng, et al.
Published: (2025)
by: Liu, Zhuocheng, et al.
Published: (2025)
Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients
by: Zuo, Shiyuan, et al.
Published: (2024)
by: Zuo, Shiyuan, et al.
Published: (2024)
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning
by: Yi, Liping, et al.
Published: (2023)
by: Yi, Liping, et al.
Published: (2023)
AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems
by: Jia, Chentao, et al.
Published: (2023)
by: Jia, Chentao, et al.
Published: (2023)
REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments
by: Desai, Humaid Ahmed, et al.
Published: (2023)
by: Desai, Humaid Ahmed, et al.
Published: (2023)
Efficient Asynchronous Federated Learning with Sparsification and Quantization
by: Jia, Juncheng, et al.
Published: (2023)
by: Jia, Juncheng, et al.
Published: (2023)
Tackling Resource-Constrained and Data-Heterogeneity in Federated Learning with Double-Weight Sparse Pack
by: Yang, Qiantao, et al.
Published: (2026)
by: Yang, Qiantao, et al.
Published: (2026)
Revisiting Ensembling in One-Shot Federated Learning
by: Allouah, Youssef, et al.
Published: (2024)
by: Allouah, Youssef, et al.
Published: (2024)
Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning
by: Wang, Zijian, et al.
Published: (2025)
by: Wang, Zijian, et al.
Published: (2025)
Private Aggregation for Byzantine-Resilient Heterogeneous Federated Learning
by: Egger, Maximilian, et al.
Published: (2025)
by: Egger, Maximilian, et al.
Published: (2025)
Reducing Communication for Split Learning by Randomized Top-k Sparsification
by: Zheng, Fei, et al.
Published: (2023)
by: Zheng, Fei, et al.
Published: (2023)
FedAgg: Adaptive Federated Learning with Aggregated Gradients
by: Yuan, Wenhao, et al.
Published: (2023)
by: Yuan, Wenhao, et al.
Published: (2023)
HDEE: Heterogeneous Domain Expert Ensemble
by: Ersoy, Oğuzhan, et al.
Published: (2025)
by: Ersoy, Oğuzhan, et al.
Published: (2025)
Heterogeneous Federated Learning with Prototype Alignment and Upscaling
by: Lee, Gyuejeong, et al.
Published: (2025)
by: Lee, Gyuejeong, et al.
Published: (2025)
Hypernetworks for Model-Heterogeneous Personalized Federated Learning
by: Zhang, Chen, et al.
Published: (2025)
by: Zhang, Chen, et al.
Published: (2025)
STHFL: Spatio-Temporal Heterogeneous Federated Learning
by: Guo, Shunxin, et al.
Published: (2025)
by: Guo, Shunxin, et al.
Published: (2025)
Federated Model Heterogeneous Matryoshka Representation Learning
by: Yi, Liping, et al.
Published: (2024)
by: Yi, Liping, et al.
Published: (2024)
FLASH: Federated Learning Across Simultaneous Heterogeneities
by: Chang, Xiangyu, et al.
Published: (2024)
by: Chang, Xiangyu, et al.
Published: (2024)
Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Network
by: Chu, Shunfeng, et al.
Published: (2024)
by: Chu, Shunfeng, et al.
Published: (2024)
10Cache: Heterogeneous Resource-Aware Tensor Caching and Migration for LLM Training
by: Afroz, Sabiha, et al.
Published: (2025)
by: Afroz, Sabiha, et al.
Published: (2025)
Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources -- A Case Study on Federated Fine-tuning of LLaMA 2
by: Li, Zilinghan, et al.
Published: (2024)
by: Li, Zilinghan, et al.
Published: (2024)
On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients
by: Keshri, Satish Kumar, et al.
Published: (2024)
by: Keshri, Satish Kumar, et al.
Published: (2024)
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning
by: Yang, Xu, et al.
Published: (2024)
by: Yang, Xu, et al.
Published: (2024)
MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes
by: Li, Xin-Chun, et al.
Published: (2024)
by: Li, Xin-Chun, et al.
Published: (2024)
FLAM: Evaluating Model Performance with Aggregatable Measures in Federated Learning
by: Stricker, Fabian, et al.
Published: (2026)
by: Stricker, Fabian, et al.
Published: (2026)
Preserving Near-Optimal Gradient Sparsification Cost for Scalable Distributed Deep Learning
by: Yoon, Daegun, et al.
Published: (2024)
by: Yoon, Daegun, et al.
Published: (2024)
Mask-Encoded Sparsification: Mitigating Biased Gradients in Communication-Efficient Split Learning
by: Zhou, Wenxuan, et al.
Published: (2024)
by: Zhou, Wenxuan, et al.
Published: (2024)
Knowledge-Driven Federated Graph Learning on Model Heterogeneity
by: Wu, Zhengyu, et al.
Published: (2025)
by: Wu, Zhengyu, et al.
Published: (2025)
An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data
by: Zhang, Jiaojiao, et al.
Published: (2025)
by: Zhang, Jiaojiao, et al.
Published: (2025)
Heterogeneous Federated Learning with Convolutional and Spiking Neural Networks
by: Yu, Yingchao, et al.
Published: (2024)
by: Yu, Yingchao, et al.
Published: (2024)
Heterogeneity: An Open Challenge for Federated On-board Machine Learning
by: Hartmann, Maria, et al.
Published: (2024)
by: Hartmann, Maria, et al.
Published: (2024)
Convergence Analysis of Split Federated Learning on Heterogeneous Data
by: Han, Pengchao, et al.
Published: (2024)
by: Han, Pengchao, et al.
Published: (2024)
Tackling Privacy Heterogeneity in Differentially Private Federated Learning
by: Xu, Ruichen, et al.
Published: (2026)
by: Xu, Ruichen, et al.
Published: (2026)
FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation
by: Zhan, Ziwei, et al.
Published: (2024)
by: Zhan, Ziwei, et al.
Published: (2024)
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated Active Learning
by: Zhang, Jun, et al.
Published: (2025)
by: Zhang, Jun, et al.
Published: (2025)
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler
by: Li, Zilinghan, et al.
Published: (2023)
by: Li, Zilinghan, et al.
Published: (2023)
Similar Items
-
Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations
by: Shao, Yumeng, et al.
Published: (2024) -
Heterogeneity-Aware Cooperative Federated Edge Learning with Adaptive Computation and Communication Compression
by: Zhang, Zhenxiao, et al.
Published: (2024) -
Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learning
by: Gao, Zhidong, et al.
Published: (2024) -
Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning
by: Balivada, Nihal, et al.
Published: (2026) -
A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks
by: Liu, Zhuocheng, et al.
Published: (2025)