SMoFi: Step-wise Momentum Fusion for Split Federated Learning on Heterogeneous Data
Fuente:
arXiv
Saved in:
| Main Authors: | Yang, Mingkun, Zhu, Ran, Wang, Qing, Yang, Jie |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Convergence Analysis of Split Federated Learning on Heterogeneous Data
by: Han, Pengchao, et al.
Published: (2024)
by: Han, Pengchao, et al.
Published: (2024)
Non-Federated Multi-Task Split Learning for Heterogeneous Sources
by: Zheng, Yilin, et al.
Published: (2024)
by: Zheng, Yilin, et al.
Published: (2024)
SemiSFL: Split Federated Learning on Unlabeled and Non-IID Data
by: Xu, Yang, et al.
Published: (2023)
by: Xu, Yang, et al.
Published: (2023)
Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting
by: Tian, Chunlin, et al.
Published: (2024)
by: Tian, Chunlin, et al.
Published: (2024)
GAS: Generative Activation-Aided Asynchronous Split Federated Learning
by: Yang, Jiarong, et al.
Published: (2024)
by: Yang, Jiarong, et al.
Published: (2024)
STHFL: Spatio-Temporal Heterogeneous Federated Learning
by: Guo, Shunxin, et al.
Published: (2025)
by: Guo, Shunxin, et al.
Published: (2025)
On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments
by: Wang, Leo Muxing, et al.
Published: (2024)
by: Wang, Leo Muxing, et al.
Published: (2024)
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)
Accelerating Wireless Distributed Learning via Hybrid Split and Federated Learning Optimization
by: Guo, Kun, et al.
Published: (2025)
by: Guo, Kun, et al.
Published: (2025)
Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data
by: Chen, Yiyue, et al.
Published: (2025)
by: Chen, Yiyue, et al.
Published: (2025)
Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness
by: Wang, Haoming, et al.
Published: (2023)
by: Wang, Haoming, et al.
Published: (2023)
An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data
by: Zhang, Jiaojiao, et al.
Published: (2025)
by: Zhang, Jiaojiao, et al.
Published: (2025)
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
by: Ji, Shaoxiong, et al.
Published: (2021)
by: Ji, Shaoxiong, et al.
Published: (2021)
FaaSMoE: A Serverless Framework for Multi-Tenant Mixture-of-Experts Serving
by: Wang, Minghe, et al.
Published: (2026)
by: Wang, Minghe, et al.
Published: (2026)
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)
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data
by: Zhang, Jiaojiao, et al.
Published: (2024)
by: Zhang, Jiaojiao, et al.
Published: (2024)
AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems
by: Jia, Chentao, et al.
Published: (2023)
by: Jia, Chentao, et al.
Published: (2023)
FedORGP: Guiding Heterogeneous Federated Learning with Orthogonality Regularization on Global Prototypes
by: Guo, Fucheng, et al.
Published: (2025)
by: Guo, Fucheng, et al.
Published: (2025)
Knowledge-Driven Federated Graph Learning on Model Heterogeneity
by: Wu, Zhengyu, et al.
Published: (2025)
by: Wu, Zhengyu, et al.
Published: (2025)
SplitFT: An Adaptive Federated Split Learning System For LLMs Fine-Tuning
by: Shan, Yimeng, et al.
Published: (2026)
by: Shan, Yimeng, et al.
Published: (2026)
Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learning
by: Yan, Dengke, et al.
Published: (2023)
by: Yan, Dengke, et al.
Published: (2023)
Hypernetworks for Model-Heterogeneous Personalized Federated Learning
by: Zhang, Chen, et al.
Published: (2025)
by: Zhang, Chen, et al.
Published: (2025)
Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices
by: Pan, Qiying, et al.
Published: (2023)
by: Pan, Qiying, et al.
Published: (2023)
Federated Model Heterogeneous Matryoshka Representation Learning
by: Yi, Liping, et al.
Published: (2024)
by: Yi, Liping, et al.
Published: (2024)
Efficient Model Compression for Hierarchical Federated Learning
by: Zhu, Xi, et al.
Published: (2024)
by: Zhu, Xi, et al.
Published: (2024)
BLOSSOM: Block-wise Federated Learning Over Shared and Sparse Observed Modalities
by: R, Pranav M, et al.
Published: (2026)
by: R, Pranav M, et al.
Published: (2026)
The Impact of Cut Layer Selection in Split Federated Learning
by: Dachille, Justin, et al.
Published: (2024)
by: Dachille, Justin, et al.
Published: (2024)
HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning
by: Kim, Gyudong, et al.
Published: (2024)
by: Kim, Gyudong, et al.
Published: (2024)
Generalization Error Analysis for Attack-Free and Byzantine-Resilient Decentralized Learning with Data Heterogeneity
by: Ye, Haoxiang, et al.
Published: (2025)
by: Ye, Haoxiang, et al.
Published: (2025)
Accelerating Hybrid Federated Learning Convergence under Partial Participation
by: Bian, Jieming, et al.
Published: (2023)
by: Bian, Jieming, et al.
Published: (2023)
CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers
by: Bian, Jieming, et al.
Published: (2023)
by: Bian, Jieming, et al.
Published: (2023)
Unlearning during Learning: An Efficient Federated Machine Unlearning Method
by: Gu, Hanlin, et al.
Published: (2024)
by: Gu, Hanlin, et al.
Published: (2024)
Communication and Computation Efficient Split Federated Learning in O-RAN
by: Gu, Shunxian, et al.
Published: (2025)
by: Gu, Shunxian, et al.
Published: (2025)
Ampere: Communication-Efficient and High-Accuracy Split Federated Learning
by: Zhang, Zihan, et al.
Published: (2025)
by: Zhang, Zihan, et al.
Published: (2025)
Guarding the Middle: Protecting Intermediate Representations in Federated Split Learning
by: Zaland, Obaidullah, et al.
Published: (2026)
by: Zaland, Obaidullah, et al.
Published: (2026)
Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients
by: Wang, Xiaolu, et al.
Published: (2024)
by: Wang, Xiaolu, 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)
Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data
by: Liu, Ji, et al.
Published: (2024)
by: Liu, Ji, et al.
Published: (2024)
Heterogeneous Federated Learning with Prototype Alignment and Upscaling
by: Lee, Gyuejeong, et al.
Published: (2025)
by: Lee, Gyuejeong, et al.
Published: (2025)
FLASH: Federated Learning Across Simultaneous Heterogeneities
by: Chang, Xiangyu, et al.
Published: (2024)
by: Chang, Xiangyu, et al.
Published: (2024)
Similar Items
-
Convergence Analysis of Split Federated Learning on Heterogeneous Data
by: Han, Pengchao, et al.
Published: (2024) -
Non-Federated Multi-Task Split Learning for Heterogeneous Sources
by: Zheng, Yilin, et al.
Published: (2024) -
SemiSFL: Split Federated Learning on Unlabeled and Non-IID Data
by: Xu, Yang, et al.
Published: (2023) -
Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting
by: Tian, Chunlin, et al.
Published: (2024) -
GAS: Generative Activation-Aided Asynchronous Split Federated Learning
by: Yang, Jiarong, et al.
Published: (2024)