Cover Image

SemiSFL: Split Federated Learning on Unlabeled and Non-IID Data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xu, Yang, Liao, Yunming, Xu, Hongli, Sun, Zhipeng, Huang, Liusheng, Qiao, Chunming
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

ParallelSFL: A Novel Split Federated Learning Framework Tackling Heterogeneity Issues
by: Liao, Yunming, et al.
Published: (2024)

MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation
by: Liao, Yunming, et al.
Published: (2023)

Towards Communication-Efficient Decentralized Federated Graph Learning over Non-IID Data
by: Wang, Shilong, et al.
Published: (2025)

Cross-region Model Training with Communication-Computation Overlapping and Delay Compensation
by: Zhu, Ying, et al.
Published: (2025)

Many Hands Make Light Work: Accelerating Edge Inference via Multi-Client Collaborative Caching
by: Liang, Wenyi, et al.
Published: (2024)

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning
by: Li, Rukuo, et al.
Published: (2025)

Collaborative Speculative Inference for Efficient LLM Inference Serving
by: Gao, Luyao, et al.
Published: (2025)

Collaborative Inference for Large Models with Task Offloading and Early Exiting
by: Xie, Zuan, et al.
Published: (2024)

SuperSFL: Resource-Heterogeneous Federated Split Learning with Weight-Sharing Super-Networks
by: Asif, Abdullah Al, et al.
Published: (2026)

BFLN: A Blockchain-based Federated Learning Model for Non-IID Data
by: Li, Yang, et al.
Published: (2024)

Accelerating End-Cloud Collaborative Inference via Near Bubble-free Pipeline Optimization
by: Gao, Luyao, et al.
Published: (2024)

Caesar: A Low-deviation Compression Approach for Efficient Federated Learning
by: Yan, Jiaming, et al.
Published: (2024)

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data
by: Ng, Jer Shyuan, et al.
Published: (2025)

Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuning
by: Huo, Yujia, et al.
Published: (2025)

DySTop
by: Shi, Yizhou, et al.
Published: (2025)

Privacy-Preserving Federated Heavy Hitter Analytics for Non-IID Data
by: Shao, Jiaqi, et al.
Published: (2023)

Optimization of Federated Learning's Client Selection for Non-IID Data Based on Grey Relational Analysis
by: Chen, Shuaijun, et al.
Published: (2023)

A Robust Federated Learning Framework for Undependable Devices at Scale
by: Wang, Shilong, et al.
Published: (2024)

FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering
by: Islam, Md Sirajul, et al.
Published: (2024)

AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks
by: Lin, Zheng, et al.
Published: (2024)

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices
by: Liu, Jun, et al.
Published: (2024)

Federated Multi-Task Learning on Non-IID Data Silos: An Experimental Study
by: Yang, Yuwen, et al.
Published: (2024)

SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Framework
by: Wu, Jiasheng, et al.
Published: (2025)

Dynamic Topology Optimization for Non-IID Data in Decentralized Learning
by: Cox, Bart, et al.
Published: (2026)

Efficient Deployment of Large Language Models on Resource-constrained Devices
by: Yao, Zhiwei, et al.
Published: (2025)

Federated Distillation on Edge Devices: Efficient Client-Side Filtering for Non-IID Data
by: Mujtaba, Ahmed, et al.
Published: (2025)

Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data
by: Morafah, Mahdi, et al.
Published: (2024)

Personalizing Federated Learning for Hierarchical Edge Networks with Non-IID Data
by: Lee, Seunghyun, et al.
Published: (2025)

Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation
by: Ma, Qianpiao, et al.
Published: (2025)

Decentralized Proactive Model Offloading and Resource Allocation for Split and Federated Learning
by: Huang, Binbin, et al.
Published: (2024)

Communication-Efficient Diffusion Strategy for Performance Improvement of Federated Learning with Non-IID Data
by: Ahn, Seyoung, et al.
Published: (2022)

Convergence Analysis of Split Federated Learning on Heterogeneous Data
by: Han, Pengchao, et al.
Published: (2024)

Learn How to Query from Unlabeled Data Streams in Federated Learning
by: Sun, Yuchang, et al.
Published: (2024)

Federated Semi-Supervised and Semi-Asynchronous Learning for Anomaly Detection in IoT Networks
by: Zhai, Wenbin, et al.
Published: (2023)

Dynamic Client Clustering, Bandwidth Allocation, and Workload Optimization for Semi-synchronous Federated Learning
by: Yu, Liangkun, et al.
Published: (2024)

Bandwidth-Aware and Cost-Efficient Pipeline Parallel Scheduling in Geo-Distributed LLM Training
by: Zhang, Han, et al.
Published: (2026)

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning
by: Hamood, Moqbel, et al.
Published: (2024)

FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data
by: Jimenez-Gutierrez, Daniel M., et al.
Published: (2026)

Leveraging MTD to Mitigate Poisoning Attacks in Decentralized FL with Non-IID Data
by: Feng, Chao, et al.
Published: (2024)

Non-Federated Multi-Task Split Learning for Heterogeneous Sources
by: Zheng, Yilin, et al.
Published: (2024)