Split Federated Learning Architectures for High-Accuracy and Low-Delay Model Training

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References (40)

[1]
PairingFL: Efficient Federated Learning With Model Splitting and Client Pairing
2025Zhiwei Yao, Ji Qi et al.
[2]
Minimization of the Training Makespan in Hybrid Federated Split Learning
2025Joana Tirana, Dimitra Tsigkari et al.
[3]
Collaborative Split Federated Learning with Parallel Training and Aggregation
2025Yiannis Papageorgiou, Y. Thomas et al.
[4]
Speed Up Federated Learning in Heterogeneous Environments: A Dynamic Tiering Approach
2025Seyed Mahmoud Sajjadi Mohammadabadi, Syed Zawad et al.
[5]
Hierarchical Split Federated Learning: Convergence Analysis and System Optimization
2024Zhengyi Lin, Wei Wei et al.
[6]
5G enabled smart cities: A real-world evaluation and analysis of 5G using a pilot smart city application
2024Abhik Banerjee, Breno G. S. Costa et al.
[7]
HFSL: heterogeneity split federated learning based on client computing capabilities
2024Nengwu Wu, Wenjie Zhao et al.
[8]
AdaptSFL: Adaptive Split Federated Learning in Resource-Constrained Edge Networks
2024Zhengyi Lin, Guanqiao Qu et al.
[9]
Workflow Optimization for Parallel Split Learning
2024Joana Tirana, Dimitra Tsigkari et al.
[10]
MP-SL: Multihop Parallel Split Learning
2024Joana Tirana, S. Lalis et al.
[11]
A Joint Communication and Learning Framework for Hierarchical Split Federated Learning
2024L. U. Khan, Mohsen Guizani et al.
[12]
FedSplitX: Federated Split Learning for Computationally-Constrained Heterogeneous Clients
2023J. Shin, Jinhyun Ahn et al.
[13]
Heterogeneous Federated Learning: State-of-the-art and Research Challenges
2023Mang Ye, Xiuwen Fang et al.
[14]
Efficient Parallel Split Learning Over Resource-Constrained Wireless Edge Networks
2023Zhengyi Lin, Guangyu Zhu et al.
[15]
A Bargaining Game for Personalized, Energy Efficient Split Learning over Wireless Networks
2022Minsu Kim, Alexander C. DeRieux et al.
[16]
CoopFL: Accelerating federated learning with DNN partitioning and offloading in heterogeneous edge computing
2022Zhiyuan Wang, Hongli Xu et al.
[17]
HSFL: An Efficient Split Federated Learning Framework via Hierarchical Organization
2022Tengxi Xia, Yongheng Deng et al.
[18]
ARES: Adaptive Resource-Aware Split Learning for Internet of Things
2022Eric Samikwa, Antonio Di Maio et al.
[19]
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning
2022Liam Collins, Hamed Hassani et al.
[20]
LocFedMix-SL: Localize, Federate, and Mix for Improved Scalability, Convergence, and Latency in Split Learning
2022Seungeun Oh, Jihong Park et al.

Showing 20 of 40 references

Founder's Pitch

"Optimize split federated learning architectures for improved accuracy, reduced delay, and lower communication overhead."

Federated LearningScore: 7View PDF ↗

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5

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