Bandwidth Slicing to Boost Federated Learning in Edge Computing

10/24/2019

∙

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.

READ FULL TEXT

Bandwidth Slicing to Boost Federated Learning in Edge Computing

Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G

Scalable Federated Learning over Passive Optical Networks

FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing

On-Device Intelligence for 5G RAN: Knowledge Transfer and Federated Learning enabled UE-Centric Traffic Steering

Energy Efficient Deployment and Orchestration of Computing Resources at the Network Edge: a Survey on Algorithms, Trends and Open Challenges

BFRT: Blockchained Federated Learning for Real-time Traffic Flow Prediction

Edge AIBench: Towards Comprehensive End-to-end Edge Computing Benchmarking

Bandwidth Slicing to Boost Federated Learning in Edge Computing

Related Research

Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G

Scalable Federated Learning over Passive Optical Networks

FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing

On-Device Intelligence for 5G RAN: Knowledge Transfer and Federated Learning enabled UE-Centric Traffic Steering

Energy Efficient Deployment and Orchestration of Computing Resources at the Network Edge: a Survey on Algorithms, Trends and Open Challenges

BFRT: Blockchained Federated Learning for Real-time Traffic Flow Prediction

Edge AIBench: Towards Comprehensive End-to-end Edge Computing Benchmarking