Cover Image

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hamood, Moqbel, Albaseer, Abdullatif, Abdallah, Mohamed, Al-Fuqaha, Ala
Format:	Preprint
Published:	2024
Subjects:	Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Empowering HWNs with Efficient Data Labeling: A Clustered Federated Semi-Supervised Learning Approach
by: Hamood, Moqbel, et al.
Published: (2024)

Optimized Federated Multitask Learning in Mobile Edge Networks: A Hybrid Client Selection and Model Aggregation Approach
by: Hamood, Moqbel, et al.
Published: (2024)

Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimization
by: Perazzone, Jake B., et al.
Published: (2025)

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

Learning to Schedule: A Supervised Learning Framework for Network-Aware Scheduling of Data-Intensive Workloads
by: Timilsina, Sankalpa, et al.
Published: (2025)

Megha: Decentralized Global Fair Scheduling for Federated Clusters
by: Thiyyakat, Meghana, et al.
Published: (2021)

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

Scheduling for On-Board Federated Learning with Satellite Clusters
by: Razmi, Nasrin, et al.
Published: (2024)

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks
by: Liu, Zhuocheng, et al.
Published: (2025)

Eva: Cost-Efficient Cloud-Based Cluster Scheduling
by: Chang, Tzu-Tao, et al.
Published: (2025)

Eventually-Consistent Federated Scheduling for Data Center Workloads
by: Thiyyakat, Meghana, et al.
Published: (2023)

Reconstruction-Based Adaptive Scheduling Using AI Inferences in Safety-Critical Systems
by: Alshaer, Samer, et al.
Published: (2025)

Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things
by: Zhang, Tinghao, et al.
Published: (2024)

A Taxonomy of Schedulers -- Operating Systems, Clusters and Big Data Frameworks
by: Sliwko, Leszek
Published: (2025)

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices
by: Chen, Xiaopei, et al.
Published: (2025)

Personalized Semi-Supervised Federated Learning for Human Activity Recognition
by: Presotto, Riccardo, et al.
Published: (2021)

Rubick: Exploiting Job Reconfigurability for Deep Learning Cluster Scheduling
by: Zhang, Xinyi, et al.
Published: (2024)

CSAFL: A Clustered Semi-Asynchronous Federated Learning Framework
by: Zhang, Yu, et al.
Published: (2021)

Update Estimation and Scheduling for Over-the-Air Federated Learning with Energy Harvesting Devices
by: Bagci, Furkan, et al.
Published: (2025)

DynLP: Parallel Dynamic Batch Update for Label Propagation in Semi-Supervised Learning
by: Shovan, S M, et al.
Published: (2026)

Measuring Data Similarity for Efficient Federated Learning: A Feasibility Study
by: Famá, Fernanda, et al.
Published: (2024)

Privacy-preserving Chunk Scheduling in a BitTorrent Implementation of Federated Learning
by: Li, Naicheng, et al.
Published: (2026)

SDFLMQ: A Semi-Decentralized Federated Learning Framework over MQTT
by: Ali-Pour, Amir, 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)

Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object Detection
by: Kim, Taehyeon, et al.
Published: (2023)

Adaptive Approach to Enhance Machine Learning Scheduling Algorithms During Runtime Using Reinforcement Learning in Metascheduling Applications
by: Alshaer, Samer, et al.
Published: (2025)

Scheduling Deep Learning Jobs in Multi-Tenant GPU Clusters via Wise Resource Sharing
by: Luo, Yizhou, et al.
Published: (2024)

SLICE: SLO-Driven Scheduling for LLM Inference on Edge Computing Devices
by: Chow, Will
Published: (2025)

FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure
by: Duan, Moming, et al.
Published: (2020)

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

FedADAS: Communication-Efficient Federated Distillation for On-Device Driver Yawn Recognition in Vehicular Networks
by: Mujtaba, Ahmed, et al.
Published: (2026)

Energy Efficient Scheduling for Serverless Systems
by: Tsenos, Michail, et al.
Published: (2024)

SemiDFL: A Semi-Supervised Paradigm for Decentralized Federated Learning
by: Liu, Xinyang, et al.
Published: (2024)

CarbonFlex: Enabling Carbon-aware Provisioning and Scheduling for Cloud Clusters
by: Hanafy, Walid A., et al.
Published: (2025)

EchoPFL: Asynchronous Personalized Federated Learning on Mobile Devices with On-Demand Staleness Control
by: Li, Xiaochen, et al.
Published: (2024)

Trustworthy Scheduling for Big Data Applications
by: Tomaras, Dimitrios, et al.
Published: (2026)

FedHC: A Hierarchical Clustered Federated Learning Framework for Satellite Networks
by: Liu, Zhuocheng, et al.
Published: (2025)

DynaFlow: Transparent and Flexible Intra-Device Parallelism via Programmable Operator Scheduling
by: Pan, Yi, et al.
Published: (2026)

Towards Energy Efficient Co-Scheduling in HPC
by: Zheng, Zhong, et al.
Published: (2026)

Efficient Probabilistic Workflow Scheduling for IaaS Clouds
by: Russo, Gabriele Russo, et al.
Published: (2024)