Cover Image

PracMHBench: Re-evaluating Model-Heterogeneous Federated Learning Based on Practical Edge Device Constraints

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Guo, Yuanchun, Liu, Bingyan, Sha, Yulong, Xian, Zhensheng
Format:	Preprint
Published:	2025
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

Federated Learning as a Service for Hierarchical Edge Networks with Heterogeneous Models
by: Gao, Wentao, et al.
Published: (2024)

Failure-Resilient Distributed Inference with Model Compression over Heterogeneous Edge Devices
by: Wang, Li, et al.
Published: (2024)

Federated Learning within Global Energy Budget over Heterogeneous Edge Accelerators
by: Banerjee, Roopkatha, et al.
Published: (2025)

Demo: A Practical Testbed for Decentralized Federated Learning on Physical Edge Devices
by: Feng, Chao, et al.
Published: (2025)

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

Heterogeneity-Aware Cooperative Federated Edge Learning with Adaptive Computation and Communication Compression
by: Zhang, Zhenxiao, et al.
Published: (2024)

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

Exploring System-Heterogeneous Federated Learning with Dynamic Model Selection
by: Yao, Dixi
Published: (2024)

Characterizing the Performance of Accelerated Jetson Edge Devices for Training Deep Learning Models
by: K., Prashanthi S., et al.
Published: (2025)

FedAPTA: Federated Multi-task Learning for Heterogeneous Devices with Adaptive Layer-wise Pruning and Task-aware Aggregation
by: Yu, Zhen, et al.
Published: (2025)

HiRL: Hierarchical Reinforcement Learning for Coordinated Resource Management in Heterogeneous Edge Computing
by: Zhu, Jianyong, et al.
Published: (2026)

Personalized Federated Learning on Data with Dynamic Heterogeneity under Limited Storage
by: Tan, Sixing, et al.
Published: (2024)

Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models
by: Cho, Yae Jee, et al.
Published: (2024)

HLoRA: Efficient Federated Learning System for LLM Heterogeneous Fine-Tuning
by: Liu, Qianli, et al.
Published: (2025)

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices
by: Yao, Dezhong, et al.
Published: (2025)

Stable-MoE: Lyapunov-based Token Routing for Distributed Mixture-of-Experts Training over Edge Networks
by: Shi, Long, et al.
Published: (2025)

Resource-efficient Parallel Split Learning in Heterogeneous Edge Computing
by: Zhang, Mingjin, et al.
Published: (2024)

Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity Learning
by: Wang, Jiaqi, et al.
Published: (2024)

Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration
by: Ma, Chengjie, et al.
Published: (2025)

GenAI at the Edge: Comprehensive Survey on Empowering Edge Devices
by: Navardi, Mozhgan, et al.
Published: (2025)

Totoro$^+$: An Adaptive and Scalable Edge Federated Learning System
by: Ching, Cheng-Wei, et al.
Published: (2026)

Federated Unlearning in Edge Networks: A Survey of Fundamentals, Challenges, Practical Applications and Future Directions
by: Ng, Jer Shyuan, et al.
Published: (2026)

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

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

FedHFT: Efficient Federated Finetuning with Heterogeneous Edge Clients
by: Ilhan, Fatih, et al.
Published: (2025)

Practical Federated Learning without a Server
by: Dhasade, Akash, et al.
Published: (2025)

Analysis and Optimization of Wireless Multimodal Federated Learning on Modal Heterogeneity
by: Han, Xuefeng, et al.
Published: (2025)

Bridging Memory Gaps: Scaling Federated Learning for Heterogeneous Clients
by: Wu, Yebo, et al.
Published: (2024)

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

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

FedFog: Resource-Aware Federated Learning in Edge and Fog Networks
by: Sobati-M, Somayeh
Published: (2025)

AIGC-assisted Federated Learning for Vehicular Edge Intelligence: Vehicle Selection, Resource Allocation and Model Augmentation
by: Qiang, Xianke, et al.
Published: (2025)

Data Heterogeneity-Aware Client Selection for Federated Learning in Wireless Networks
by: Yang, Yanbing, et al.
Published: (2025)

DFPL: Decentralized Federated Prototype Learning Across Heterogeneous Data Distributions
by: Zhang, Hongliang, et al.
Published: (2025)

FedCache: A Knowledge Cache-driven Federated Learning Architecture for Personalized Edge Intelligence
by: Wu, Zhiyuan, et al.
Published: (2023)

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training
by: Mehboob, Talha, et al.
Published: (2025)

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

Surviving the Edge: Federated Learning under Networking and Resource Constraints
by: Mwanje, Mike, et al.
Published: (2026)

Heterogeneity-Aware Memory Efficient Federated Learning via Progressive Layer Freezing
by: Yebo, Wu, et al.
Published: (2024)

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