HO-SFL: Hybrid-Order Split Federated Learning with Backprop-Free Clients and Dimension-Free Aggregation
Fuente:
arXiv
Guardado en:
| Autores principales: | Chen, Qiyuan, Wu, Xian, Wang, Yi, Chen, Xianhao |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks
por: Lin, Zheng, et al.
Publicado: (2024)
por: Lin, Zheng, et al.
Publicado: (2024)
Aggregation Alignment for Federated Learning with Mixture-of-Experts under Data Heterogeneity
por: Fang, Zihan, et al.
Publicado: (2026)
por: Fang, Zihan, et al.
Publicado: (2026)
BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregation
por: Xie, Yuhan, et al.
Publicado: (2026)
por: Xie, Yuhan, et al.
Publicado: (2026)
An Interpretable Client Decision Tree Aggregation process for Federated Learning
por: Argente-Garrido, Alberto, et al.
Publicado: (2024)
por: Argente-Garrido, Alberto, et al.
Publicado: (2024)
HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems
por: Lin, Zheng, et al.
Publicado: (2025)
por: Lin, Zheng, et al.
Publicado: (2025)
Enhancing Federated Learning Through Secure Cluster-Weighted Client Aggregation
por: Ranaweera, Kanishka, et al.
Publicado: (2025)
por: Ranaweera, Kanishka, et al.
Publicado: (2025)
Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities
por: Lin, Zheng, et al.
Publicado: (2023)
por: Lin, Zheng, et al.
Publicado: (2023)
Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning
por: Wang, Zijian, et al.
Publicado: (2025)
por: Wang, Zijian, et al.
Publicado: (2025)
Learning Unlabeled Clients Divergence for Federated Semi-Supervised Learning via Anchor Model Aggregation
por: Elbatel, Marawan, et al.
Publicado: (2024)
por: Elbatel, Marawan, et al.
Publicado: (2024)
ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices
por: Zhu, Guangyu, et al.
Publicado: (2024)
por: Zhu, Guangyu, et al.
Publicado: (2024)
FedEmb: A Vertical and Hybrid Federated Learning Algorithm using Network And Feature Embedding Aggregation
por: Meng, Fanfei, et al.
Publicado: (2023)
por: Meng, Fanfei, et al.
Publicado: (2023)
Data-Free Client Contribution Estimation via Logit Maximization for Federated Learning
por: Ukaye, Asim, et al.
Publicado: (2026)
por: Ukaye, Asim, et al.
Publicado: (2026)
Federated Learning with Sample-level Client Drift Mitigation
por: Xu, Haoran, et al.
Publicado: (2025)
por: Xu, Haoran, et al.
Publicado: (2025)
SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation
por: Zhong, Luying, et al.
Publicado: (2024)
por: Zhong, Luying, et al.
Publicado: (2024)
HealSplit: Towards Self-Healing through Adversarial Distillation in Split Federated Learning
por: Xie, Yuhan, et al.
Publicado: (2025)
por: Xie, Yuhan, et al.
Publicado: (2025)
Hierarchical Split Federated Learning: Convergence Analysis and System Optimization
por: Lin, Zheng, et al.
Publicado: (2024)
por: Lin, Zheng, et al.
Publicado: (2024)
Programming by Backprop: An Instruction is Worth 100 Examples When Finetuning LLMs
por: Cook, Jonathan, et al.
Publicado: (2025)
por: Cook, Jonathan, et al.
Publicado: (2025)
Warming Up for Zeroth-Order Federated Pre-Training with Low Resource Clients
por: Legate, Gwen, et al.
Publicado: (2025)
por: Legate, Gwen, et al.
Publicado: (2025)
Tackling Noisy Clients in Federated Learning with End-to-end Label Correction
por: Jiang, Xuefeng, et al.
Publicado: (2024)
por: Jiang, Xuefeng, et al.
Publicado: (2024)
Buffered Asynchronous Secure Aggregation for Cross-Device Federated Learning
por: Wang, Kun, et al.
Publicado: (2024)
por: Wang, Kun, et al.
Publicado: (2024)
Hybrid Federated and Split Learning for Privacy Preserving Clinical Prediction and Treatment Optimization
por: Akter, Farzana, et al.
Publicado: (2026)
por: Akter, Farzana, et al.
Publicado: (2026)
Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models
por: Fang, Zihan, et al.
Publicado: (2024)
por: Fang, Zihan, et al.
Publicado: (2024)
Mobile Edge Intelligence for Large Language Models: A Contemporary Survey
por: Qu, Guanqiao, et al.
Publicado: (2024)
por: Qu, Guanqiao, et al.
Publicado: (2024)
FedCCRL: Federated Domain Generalization with Cross-Client Representation Learning
por: Wang, Xinpeng, et al.
Publicado: (2024)
por: Wang, Xinpeng, et al.
Publicado: (2024)
Operator-Theoretic Framework for Gradient-Free Federated Learning
por: Kumar, Mohit, et al.
Publicado: (2025)
por: Kumar, Mohit, et al.
Publicado: (2025)
FLClear: Visually Verifiable Multi-Client Watermarking for Federated Learning
por: Gu, Chen, et al.
Publicado: (2025)
por: Gu, Chen, et al.
Publicado: (2025)
Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser Scheme
por: Long, Qianyu, et al.
Publicado: (2024)
por: Long, Qianyu, et al.
Publicado: (2024)
Communication-Efficient Federated Learning with Accelerated Client Gradient
por: Kim, Geeho, et al.
Publicado: (2022)
por: Kim, Geeho, et al.
Publicado: (2022)
Unlearning Clients, Features and Samples in Vertical Federated Learning
por: Varshney, Ayush K., et al.
Publicado: (2025)
por: Varshney, Ayush K., et al.
Publicado: (2025)
On-Device Fine-Tuning via Backprop-Free Zeroth-Order Optimization
por: Katti, Prabodh, et al.
Publicado: (2025)
por: Katti, Prabodh, et al.
Publicado: (2025)
SplAgger: Split Aggregation for Meta-Reinforcement Learning
por: Beck, Jacob, et al.
Publicado: (2024)
por: Beck, Jacob, et al.
Publicado: (2024)
Towards Energy-Aware Federated Learning via MARL: A Dual-Selection Approach for Model and Client
por: Xia, Jun, et al.
Publicado: (2024)
por: Xia, Jun, et al.
Publicado: (2024)
Axial Neural Networks for Dimension-Free Foundation Models
por: Kim, Hyunsu, et al.
Publicado: (2025)
por: Kim, Hyunsu, et al.
Publicado: (2025)
Federated Linear Contextual Bandits with Heterogeneous Clients
por: Blaser, Ethan, et al.
Publicado: (2024)
por: Blaser, Ethan, et al.
Publicado: (2024)
FedRG: Unleashing the Representation Geometry for Federated Learning with Noisy Clients
por: Wen, Tian, et al.
Publicado: (2026)
por: Wen, Tian, et al.
Publicado: (2026)
Generating density nowcasts for U.S. GDP growth with deep learning: Bayes by Backprop and Monte Carlo dropout
por: Németh, Kristóf, et al.
Publicado: (2024)
por: Németh, Kristóf, et al.
Publicado: (2024)
FedZMG: Efficient Client-Side Optimization in Federated Learning
por: Zantalis, Fotios, et al.
Publicado: (2026)
por: Zantalis, Fotios, et al.
Publicado: (2026)
Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learning
por: Weng, Shudi, et al.
Publicado: (2025)
por: Weng, Shudi, et al.
Publicado: (2025)
Over-the-Air Federated Learning in Cell-Free MIMO with Long-term Power Constraint
por: Wang, Yifan, et al.
Publicado: (2024)
por: Wang, Yifan, et al.
Publicado: (2024)
FedCore: Straggler-Free Federated Learning with Distributed Coresets
por: Guo, Hongpeng, et al.
Publicado: (2024)
por: Guo, Hongpeng, et al.
Publicado: (2024)
Ejemplares similares
-
AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks
por: Lin, Zheng, et al.
Publicado: (2024) -
Aggregation Alignment for Federated Learning with Mixture-of-Experts under Data Heterogeneity
por: Fang, Zihan, et al.
Publicado: (2026) -
BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregation
por: Xie, Yuhan, et al.
Publicado: (2026) -
An Interpretable Client Decision Tree Aggregation process for Federated Learning
por: Argente-Garrido, Alberto, et al.
Publicado: (2024) -
HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems
por: Lin, Zheng, et al.
Publicado: (2025)