English

Bandwidth Slicing to Boost Federated Learning in Edge Computing

Networking and Internet Architecture 2019-11-19 v1 Distributed, Parallel, and Cluster Computing Machine Learning
Authors: Jun Li , Xiaoman Shen , Lei Chen , Jiajia Chen
View on arXiv ↗ PDF ↗

Abstract

Bandwidth slicing is introduced to support federated learning in edge computing to assure low communication delay for training traffic. Results reveal that bandwidth slicing significantly improves training efficiency while achieving good learning accuracy.

Keywords

network slicing network protocol

Cite

@article{arxiv.1911.07615,
  title  = {Bandwidth Slicing to Boost Federated Learning in Edge Computing},
  author = {Jun Li and Xiaoman Shen and Lei Chen and Jiajia Chen},
  journal= {arXiv preprint arXiv:1911.07615},
  year   = {2019}
}

Comments

Conference,3 pages, 3 figures

Related papers

View all related →
Machine Learning · Computer Science
Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks
Junhe Zhang, Wanli Ni, Dongyu Wang
2024-12-11
Machine Learning · Computer Science
Split Learning in 6G Edge Networks
Zheng Lin, Guanqiao Qu, Xianhao Chen, Kaibin Huang
2024-01-25
Machine Learning · Computer Science
Edge Intelligence Over the Air: Two Faces of Interference in Federated Learning
Zihan Chen, Howard H. Yang, Tony Q. S. Quek
2023-06-21
Machine Learning · Computer Science
GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning
Shiqi He, Qifan Yan, Feijie Wu, Lanjun Wang +2
2022-12-06
Networking and Internet Architecture · Computer Science
On the Benefits of Coding for Network Slicing
Homa Esfahanizadeh, Vipindev Adat Vasudevan, Benjamin D. Kim, Shruti Siva +3
2024-04-30
Networking and Internet Architecture · Computer Science
Learning-Assisted Secure End-to-End Network Slicing for Cyber-Physical Systems
Qiang Liu, Tao Han, Nirwan Ansari
2019-10-31
Cryptography and Security · Computer Science
Constrained Differentially Private Federated Learning for Low-bandwidth Devices
Raouf Kerkouche, Gergely Ács, Claude Castelluccia, Pierre Genevès
2021-03-02
Signal Processing · Electrical Eng. & Systems
Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G
Shashank Jere, Qiang Fan, Bodong Shang, Lianjun Li +1
2020-07-21
Machine Learning · Computer Science
Personalizing Federated Learning with Over-the-Air Computations
Zihan Chen, Zeshen Li, Howard H. Yang, Tony Q. S. Quek
2023-02-27
Networking and Internet Architecture · Computer Science
Slicing for AI: An Online Learning Framework for Network Slicing Supporting AI Services
Menna Helmy, Alaa Awad Abdellatif, Naram Mhaisen, Amr Mohamed +1
2024-11-06
Machine Learning · Computer Science
Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices
Hangyu Li, Hongyue Wu, Guodong Fan, Zhen Zhang +2
2025-08-04
Distributed, Parallel, and Cluster Computing · Computer Science
AI on the Edge: Rethinking AI-based IoT Applications Using Specialized Edge Architectures
Qianlin Liang, Prashant Shenoy, David Irwin
2020-03-30
Machine Learning · Computer Science
Towards Communication-Learning Trade-off for Federated Learning at the Network Edge
Jianyang Ren, Wanli Ni, Hui Tian
2022-05-31
Distributed, Parallel, and Cluster Computing · Computer Science
Management and Orchestration of Network Slices in 5G, Fog, Edge and Clouds
Adel Nadjaran Toosi, Redowan Mahmud, Qinghua Chi, Rajkumar Buyya
2018-12-04
Networking and Internet Architecture · Computer Science
Slicing-Based AI Service Provisioning on Network Edge
Mushu Li, Jie Gao, Conghao Zhou, Xuemin +2
2021-05-18
Distributed, Parallel, and Cluster Computing · Computer Science
Training on the Edge: The why and the how
Navjot Kukreja, Alena Shilova, Olivier Beaumont, Jan Huckelheim +3
2019-03-08
Networking and Internet Architecture · Computer Science
EdgeSlice: Slicing Wireless Edge Computing Network with Decentralized Deep Reinforcement Learning
Qiang Liu, Tao Han, Ephraim Moges
2020-03-31
Networking and Internet Architecture · Computer Science
To Edge or Not to Edge?
Faria Kalim, Shadi A. Noghabi, Shiv Verma
2018-03-23
Machine Learning · Computer Science
FedGrad: Optimisation in Decentralised Machine Learning
Mann Patel
2022-11-28
Machine Learning · Computer Science
Toward efficient resource utilization at edge nodes in federated learning
Sadi Alawadi, Addi Ait-Mlouk, Salman Toor, Andreas Hellander
2024-06-13
Artificial Intelligence · Computer Science
On Enhancing Network Throughput using Reinforcement Learning in Sliced Testbeds
Daniel Pereira Monteiro, Lucas Nardelli de Freitas Botelho Saar, Larissa Ferreira Rodrigues Moreira, Rodrigo Moreira
2024-12-24
Machine Learning · Computer Science
Analysis and Optimal Edge Assignment For Hierarchical Federated Learning on Non-IID Data
Naram Mhaisen, Alaa Awad, Amr Mohamed, Aiman Erbad +1
2021-02-04
Machine Learning · Computer Science
Overlay-based Decentralized Federated Learning in Bandwidth-limited Networks
Yudi Huang, Tingyang Sun, Ting He
2024-08-12
Networking and Internet Architecture · Computer Science
Distributed Resource Allocation for Network Slicing of Bandwidth and Computational Resource
Anqi Huang, Yingyu Li, Yong Xiao, Xiaohu Ge +2
2020-02-05
Networking and Internet Architecture · Computer Science
Device Scheduling with Fast Convergence for Wireless Federated Learning
Wenqi Shi, Sheng Zhou, Zhisheng Niu
2019-11-05
R2 v1 2026-06-23T12:19:10.526Z