paper-with-me

홈 › Papers

Bandwidth Slicing to Boost Federated Learning in Edge Computing

2019-10-24 · Jun Li, Xiaoman Shen, Lei Chen, Jiajia Chen

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.

📄 PDF Abstract BibTeX arXiv:1911.07615

Code (0)

등록된 구현이 없습니다.

Tasks

Edge-computingFederated Learning

Similar Papers 제목 키워드 기반

Multiple Access in the Era of Distributed Computing and Edge Intelligence

2024-02-26 · Nikos G. Evgenidis, Nikos A. Mitsiou, Vasiliki I. Koutsioumpa, Sotiris A. Tegos 외

This paper focuses on the latest research and innovations in fundamental next-generation multiple access (NGMA) techniques and the coexistence with other key technologies for the sixth generation (6G) of wireless network…

Distributed ComputingEdge-computingFederated Learning

Service Provisioning Framework for RAN Slicing: User Admissibility, Slice Association and Bandwidth Allocation

2020-06-08 · IEEE Transactions on Mobile Computing 2020 6 · Yao Sun, Shuang Qin, Member, Gang Feng 외

Network slicing (NS) has been identified as one of the most promising architectural technologies for future mobile network systems to meet the extremely diversified service requirements of users. In radio access networks …

Towards Quantum-Enabled 6G Slicing

2022-10-21 · Farhad Rezazadeh, Sarang Kahvazadeh, Mohammadreza Mosahebfard

The quantum machine learning (QML) paradigms and their synergies with network slicing can be envisioned to be a disruptive technology on the cusp of entering to era of sixth-generation (6G), where the mobile communicatio…

Deep Reinforcement LearningFederated LearningQuantum Machine Learningreinforcement-learning+1

HierSFL: Local Differential Privacy-aided Split Federated Learning in Mobile Edge Computing

2024-01-16 · Minh K. Quan, Dinh C. Nguyen, Van-Dinh Nguyen, Mayuri Wijayasundara 외

Federated Learning is a promising approach for learning from user data while preserving data privacy. However, the high requirements of the model training process make it difficult for clients with limited memory or band…

Edge-computingFederated Learning

Adaptive Resource Management for Edge Network Slicing using Incremental Multi-Agent Deep Reinforcement Learning

2023-10-26 · Haiyuan Li, Yuelin Liu, Xueqing Zhou, Xenofon Vasilakos 외

Multi-access edge computing provides local resources in mobile networks as the essential means for meeting the demands of emerging ultra-reliable low-latency communications. At the edge, dynamic computing requests requir…

Deep Reinforcement LearningEdge-computingIncremental LearningManagement+1