A Federated Reinforcement Learning Framework via a Committee Mechanism for Resource Management in 5G Networks
This paper proposes a novel decentralized federated reinforcement learning (DFRL) framework that integrates deep reinforcement learning (DRL) with decentralized federated learning (DFL). The DFRL framework boosts efficient virtual instance scaling in Mobile Edge Computing (MEC) environments for 5G c...
Saved in:
| Main Authors: | Jaewon Jeong, Joohyung Lee |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/21/7031 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Federated Reinforcement Learning-Based Dynamic Resource Allocation and Task Scheduling in Edge for IoT Applications
by: Saroj Mali, et al.
Published: (2025-03-01) -
Role, function, and expectations of research funding committees: Perspectives from committee members [version 2; peer review: 1 approved, 2 approved with reservations, 1 not approved]
by: Amanda Blatch-Jones, et al.
Published: (2025-01-01) -
Deep reinforcement learning based resource provisioning for federated edge learning
by: Xingyun Chen, et al.
Published: (2025-06-01) -
Governance and Management Accounting: Board Size, Environmental Committee, and Audit Committee on Environmental Performance
by: Retnoningrum Hidayah, et al.
Published: (2025-03-01) -
Does audit committee characteristics a driver in risk disclosure?
by: Medina Almunawwaroh, et al.
Published: (2023-12-01)