Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning
Author:
Affiliation:
1. School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China
2. School of Electronics and Computer Science, University of Southampton, Southampton, U.K.
Funder
National Natural Science Foundation of China
Open Research Fund of National Mobile Communications Research Laboratory
Southeast University
Engineering and Physical Sciences Research Council Projects
European Research Council's Advanced Fellow Grant QuantCom
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering
Link
http://xplorestaging.ieee.org/ielx7/25/10155543/10032111.pdf?arnumber=10032111
Reference20 articles.
1. When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multitimescale Resource Management for Multiaccess Edge Computing in 5G Ultradense Network
2. Federated Deep Reinforcement Learning for Internet of Things With Decentralized Cooperative Edge Caching
3. Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
4. Semi-Distributed Resource Management in UAV-Aided MEC Systems: A Multi-Agent Federated Reinforcement Learning Approach
5. Mobility Models, Traces and Impact of Mobility on Opportunistic Routing Algorithms: A Survey
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