Copertina

Navigating High-Degree Heterogeneity: Federated Learning in Aerial and Space Networks

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Dong, Fan, Leung, Henry, Drew, Steve
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Distributed, Parallel, and Cluster Computing
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Federated Learning Model Aggregation in Heterogenous Aerial and Space Networks
di: Dong, Fan, et al.
Pubblicazione: (2023)

FedGreen: Carbon-aware Federated Learning with Model Size Adaptation
di: Abbasi, Ali, et al.
Pubblicazione: (2024)

Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey
di: Wu, Jiajun, et al.
Pubblicazione: (2023)

Semi-decentralized Federated Time Series Prediction with Client Availability Budgets
di: Bao, Yunkai, et al.
Pubblicazione: (2025)

Heterogeneous Federated Learning with Convolutional and Spiking Neural Networks
di: Yu, Yingchao, et al.
Pubblicazione: (2024)

Towards Carbon-Aware Container Orchestration: Predicting Workload Energy Consumption with Federated Learning
di: Saad, Zainab, et al.
Pubblicazione: (2025)

Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks
di: Wang, Shuai, et al.
Pubblicazione: (2025)

Federated Model Heterogeneous Matryoshka Representation Learning
di: Yi, Liping, et al.
Pubblicazione: (2024)

FLASH: Federated Learning Across Simultaneous Heterogeneities
di: Chang, Xiangyu, et al.
Pubblicazione: (2024)

Heterogeneous Federated Learning with Prototype Alignment and Upscaling
di: Lee, Gyuejeong, et al.
Pubblicazione: (2025)

Hypernetworks for Model-Heterogeneous Personalized Federated Learning
di: Zhang, Chen, et al.
Pubblicazione: (2025)

STHFL: Spatio-Temporal Heterogeneous Federated Learning
di: Guo, Shunxin, et al.
Pubblicazione: (2025)

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

Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients
di: Zuo, Shiyuan, et al.
Pubblicazione: (2024)

Heterogeneity: An Open Challenge for Federated On-board Machine Learning
di: Hartmann, Maria, et al.
Pubblicazione: (2024)

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

Knowledge-Driven Federated Graph Learning on Model Heterogeneity
di: Wu, Zhengyu, et al.
Pubblicazione: (2025)

An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data
di: Zhang, Jiaojiao, et al.
Pubblicazione: (2025)

Tackling Privacy Heterogeneity in Differentially Private Federated Learning
di: Xu, Ruichen, et al.
Pubblicazione: (2026)

Federated Incomplete Multi-View Clustering with Heterogeneous Graph Neural Networks
di: Yan, Xueming, et al.
Pubblicazione: (2024)

Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients
di: Wang, Xiaolu, et al.
Pubblicazione: (2024)

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

Apodotiko: Enabling Efficient Serverless Federated Learning in Heterogeneous Environments
di: Chadha, Mohak, et al.
Pubblicazione: (2024)

On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments
di: Wang, Leo Muxing, et al.
Pubblicazione: (2024)

Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations
di: Shao, Yumeng, et al.
Pubblicazione: (2024)

Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data
di: Zhang, Jiaojiao, et al.
Pubblicazione: (2024)

Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning
di: Balivada, Nihal, et al.
Pubblicazione: (2026)

Resource-Aware Aggregation and Sparsification in Heterogeneous Ensemble Federated Learning
di: Ryum, Keumseo, et al.
Pubblicazione: (2025)

Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data
di: Chen, Yiyue, et al.
Pubblicazione: (2025)

Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources -- A Case Study on Federated Fine-tuning of LLaMA 2
di: Li, Zilinghan, et al.
Pubblicazione: (2024)

Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not?
di: Zhao, Yi, et al.
Pubblicazione: (2026)

Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training
di: Wu, Yebo, et al.
Pubblicazione: (2024)

A Unified Solution to Diverse Heterogeneities in One-shot Federated Learning
di: Bai, Jun, et al.
Pubblicazione: (2024)

Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting
di: Tian, Chunlin, et al.
Pubblicazione: (2024)

Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices
di: Pan, Qiying, et al.
Pubblicazione: (2023)

Federated Learning Framework for Scalable AI in Heterogeneous HPC and Cloud Environments
di: Ghimire, Sangam, et al.
Pubblicazione: (2025)

Local Gradient Regulation Stabilizes Federated Learning under Client Heterogeneity
di: Luo, Ping, et al.
Pubblicazione: (2026)

Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness
di: Wang, Haoming, et al.
Pubblicazione: (2023)

FedPref: Federated Learning Across Heterogeneous Multi-objective Preferences
di: Hartmann, Maria, et al.
Pubblicazione: (2025)

Device Association and Resource Allocation for Hierarchical Split Federated Learning in Space-Air-Ground Integrated Network
di: Zhao, Haitao, et al.
Pubblicazione: (2026)