Cover Image

FedAuxHMTL: Federated Auxiliary Hard-Parameter Sharing Multi-Task Learning for Network Edge Traffic Classification

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ahmed, Faisal, Lee, Myungjin, Subramaniam, Suresh, Matsuura, Motoharu, Hasegawa, Hiroshi, Lin, Shih-Chun
Format:	Preprint
Published:	2024
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

Digital Twin-Empowered Deep Reinforcement Learning for Intelligent VNF Migration in Edge-Core Networks
by: Ahmed, Faisal, et al.
Published: (2025)

Federated Deep Reinforcement Learning-Driven O-RAN for Automatic Multirobot Reconfiguration
by: Ahmed, Faisal, et al.
Published: (2025)

Digital Twin Enabled Data-Driven Approach for Traffic Efficiency and Software-Defined Vehicular Network Optimization
by: Shahriar, Mohammad Sajid, et al.
Published: (2024)

Intelligent Edge Resource Provisioning for Scalable Digital Twins of Autonomous Vehicles
by: Shahriar, Mohammad Sajid, et al.
Published: (2025)

Prioritized Multi-Tenant Traffic Engineering for Dynamic QoS Provisioning in Autonomous SDN-OpenFlow Edge Networks
by: Shahriar, Mohammad Sajid, et al.
Published: (2024)

FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing
by: Jia, Yongzhe, et al.
Published: (2024)

A benchmarking framework for PON-based fronthaul network design
by: Erbayat, Egemen, et al.
Published: (2026)

FedMOA: Federated GRPO for Personalized Reasoning LLMs under Heterogeneous Rewards
by: Wang, Ziyao, et al.
Published: (2026)

FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles
by: Zhai, Yijun, et al.
Published: (2024)

SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-Device Inference
by: Khare, Alind, et al.
Published: (2023)

FedPM: Federated Learning Using Second-order Optimization with Preconditioned Mixing of Local Parameters
by: Ishii, Hiro, et al.
Published: (2025)

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

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

FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs
by: Chen, Zihan, et al.
Published: (2025)

LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learning
by: Qi, Shixiong, et al.
Published: (2024)

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

Parameter-Efficient and Personalized Federated Training of Generative Models at the Edge
by: Khan, Kabir, et al.
Published: (2025)

FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout
by: Feng, Zhiying, et al.
Published: (2023)

FedLog: Personalized Federated Classification with Less Communication and More Flexibility
by: Yu, Haolin, et al.
Published: (2024)

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

FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning
by: Bai, Yuxuan, 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)

On Harnessing Idle Compute at the Edge for Foundation Model Training
by: Xue, Leyang, et al.
Published: (2025)

Raft Distributed System for Multi-access Edge Computing Sharing Resources
by: Khaliq, Zain, et al.
Published: (2024)

S2M3: Split-and-Share Multi-Modal Models for Distributed Multi-Task Inference on the Edge
by: Yoon, JinYi, et al.
Published: (2025)

FedMLAC: Mutual Learning Driven Heterogeneous Federated Audio Classification
by: Bai, Jun, et al.
Published: (2025)

Flame: Simplifying Topology Extension in Federated Learning
by: Daga, Harshit, et al.
Published: (2023)

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

Enabling Elastic Model Serving with MultiWorld
by: Lee, Myungjin, et al.
Published: (2024)

Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study
by: Baumgart, Gustav A., et al.
Published: (2024)

FedFQ: Federated Learning with Fine-Grained Quantization
by: Li, Haowei, et al.
Published: (2024)

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

Dora: QoE-Aware Hybrid Parallelism for Distributed Edge AI
by: Jin, Jianli, et al.
Published: (2025)

Closing the Generalization Gap in Parameter-efficient Federated Edge Learning
by: Du, Xinnong, et al.
Published: (2025)

FedOptimus: Optimizing Vertical Federated Learning for Scalability and Efficiency
by: Shrivastava, Nikita, et al.
Published: (2025)

OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPC
by: Tyagi, Sahil, et al.
Published: (2025)

FedHB: Hierarchical Bayesian Federated Learning
by: Kim, Minyoung, et al.
Published: (2023)

FedAST: Federated Asynchronous Simultaneous Training
by: Askin, Baris, et al.
Published: (2024)

EcoFed: Efficient Communication for DNN Partitioning-based Federated Learning
by: Wu, Di, et al.
Published: (2023)

FedCostAware: Enabling Cost-Aware Federated Learning on the Cloud
by: Sinha, Aditya, et al.
Published: (2025)