Image de couverture de livre

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Liu, Zhuocheng, Shen, Zhishu, Zheng, Qiushi, Zhang, Tiehua, Lei, Zheng, Jin, Jiong
Format:	Preprint
Publié:	2025
Sujets:	Distributed, Parallel, and Cluster Computing Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

FedHC: A Hierarchical Clustered Federated Learning Framework for Satellite Networks
par: Liu, Zhuocheng, et autres
Publié: (2025)

Intelligent Task Management via Dynamic Multi-region Division in LEO Satellite Networks
par: Song, Zixuan, et autres
Publié: (2025)

Collaborative UAVs Multi-task Video Processing Optimization Based on Enhanced Distributed Actor-Critic Networks
par: Rong, Ziqi, et autres
Publié: (2024)

A Sharded Blockchain-Based Secure Federated Learning Framework for LEO Satellite Networks
par: Wu, Wenbo, et autres
Publié: (2024)

CCRSat: A Collaborative Computation Reuse Framework for Satellite Edge Computing Networks
par: Zhang, Ye, et autres
Publié: (2025)

Unleashing Collaborative Computing for Adaptive Video Streaming with Multi-objective Optimization in Satellite Terrestrial Networks
par: Shen, Zhishu, et autres
Publié: (2024)

Collaborative Satellite Computing through Adaptive DNN Task Splitting and Offloading
par: Peng, Shifeng, et autres
Publié: (2024)

A Structure-Agnostic Co-Tuning Framework for LLMs and SLMs in Cloud-Edge Systems
par: Liu, Yuze, et autres
Publié: (2025)

LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data
par: Zhang, Yuxin, et autres
Publié: (2025)

ML-ECS: A Collaborative Multimodal Learning Framework for Edge-Cloud Synergies
par: Liu, Yuze, et autres
Publié: (2026)

FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data
par: Zhang, Yuxin, et autres
Publié: (2024)

FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks
par: Lin, Zheng, et autres
Publié: (2023)

CSAFL: A Clustered Semi-Asynchronous Federated Learning Framework
par: Zhang, Yu, et autres
Publié: (2021)

Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations
par: Shao, Yumeng, et autres
Publié: (2024)

Federated Incomplete Multi-View Clustering with Heterogeneous Graph Neural Networks
par: Yan, Xueming, et autres
Publié: (2024)

SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework
par: Zhang, Yuxin, et autres
Publié: (2024)

Scheduling for On-Board Federated Learning with Satellite Clusters
par: Razmi, Nasrin, et autres
Publié: (2024)

Federated Semi-Supervised and Semi-Asynchronous Learning for Anomaly Detection in IoT Networks
par: Zhai, Wenbin, et autres
Publié: (2023)

Non-Federated Multi-Task Split Learning for Heterogeneous Sources
par: Zheng, Yilin, et autres
Publié: (2024)

Resource-Aware Aggregation and Sparsification in Heterogeneous Ensemble Federated Learning
par: Ryum, Keumseo, et autres
Publié: (2025)

Heterogeneous Federated Learning with Convolutional and Spiking Neural Networks
par: Yu, Yingchao, et autres
Publié: (2024)

Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients
par: Zuo, Shiyuan, et autres
Publié: (2024)

Personalized Semi-Supervised Federated Learning for Human Activity Recognition
par: Presotto, Riccardo, et autres
Publié: (2021)

FedQS: Optimizing Gradient and Model Aggregation for Semi-Asynchronous Federated Learning
par: Li, Yunbo, et autres
Publié: (2025)

Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments
par: Sun, Pengcheng, et autres
Publié: (2024)

SemiDFL: A Semi-Supervised Paradigm for Decentralized Federated Learning
par: Liu, Xinyang, et autres
Publié: (2024)

FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning
par: Yi, Liping, et autres
Publié: (2023)

Federated Learning as a Service for Hierarchical Edge Networks with Heterogeneous Models
par: Gao, Wentao, et autres
Publié: (2024)

LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite Networks
par: Lin, Zheng, et autres
Publié: (2025)

Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning
par: Wang, Zijian, et autres
Publié: (2025)

A Bayesian Framework for Clustered Federated Learning
par: Wu, Peng, et autres
Publié: (2024)

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning
par: Hamood, Moqbel, et autres
Publié: (2024)

Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learning
par: Zhu, Guogang, et autres
Publié: (2024)

Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not?
par: Zhao, Yi, et autres
Publié: (2026)

On Resilient and Efficient Linear Secure Aggregation in Hierarchical Federated Learning
par: Weng, Shudi, et autres
Publié: (2026)

Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models
par: Cho, Yae Jee, et autres
Publié: (2024)

Asyn2F: An Asynchronous Federated Learning Framework with Bidirectional Model Aggregation
par: Cao, Tien-Dung, et autres
Publié: (2024)

Private Aggregation for Byzantine-Resilient Heterogeneous Federated Learning
par: Egger, Maximilian, et autres
Publié: (2025)

A Robust Federated Learning Framework for Undependable Devices at Scale
par: Wang, Shilong, et autres
Publié: (2024)

Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning
par: Elmahallawy, Mohamed, et autres
Publié: (2024)