Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2301.11014v1
• Date: Thu, 26 Jan 2023 10:09:23 GMT
• Title: Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning
• Authors: Yan Lin, Jinming Bao, Yijin Zhang, Jun Li, Feng Shu and Lajos Hanzo
• Abstract summary: We investigate the privacy-preserving joint edge association and power allocation problem. The proposed solution strikes a compelling trade-off, while preserving a higher privacy level than the state-of-the-art solutions.
• Score: 74.53077322713548
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Proactive edge association is capable of improving wireless connectivity at the cost of increased handover (HO) frequency and energy consumption, while relying on a large amount of private information sharing required for decision making. In order to improve the connectivity-cost trade-off without privacy leakage, we investigate the privacy-preserving joint edge association and power allocation (JEAPA) problem in the face of the environmental uncertainty and the infeasibility of individual learning. Upon modelling the problem by a decentralized partially observable Markov Decision Process (Dec-POMDP), it is solved by federated multi-agent reinforcement learning (FMARL) through only sharing encrypted training data for federatively learning the policy sought. Our simulation results show that the proposed solution strikes a compelling trade-off, while preserving a higher privacy level than the state-of-the-art solutions.

Related papers

• Privacy in Federated Learning [0.0]
  Federated Learning (FL) represents a significant advancement in distributed machine learning. This chapter delves into the core privacy concerns within FL, including the risks of data reconstruction, model inversion attacks, and membership inference. It examines the trade-offs between model accuracy and privacy, emphasizing the importance of balancing these factors in practical implementations.
  arXiv  Detail & Related papers  (2024-08-12T18:41:58Z)
• Federated Learning-Empowered AI-Generated Content in Wireless Networks [58.48381827268331]
  Federated learning (FL) can be leveraged to improve learning efficiency and achieve privacy protection for AIGC. We present FL-based techniques for empowering AIGC, and aim to enable users to generate diverse, personalized, and high-quality content.
  arXiv  Detail & Related papers  (2023-07-14T04:13:11Z)
• Over-the-Air Federated Averaging with Limited Power and Privacy Budgets [49.04036552090802]
  This paper studies a private over-the-air federated averaging (DP-OTA-FedAvg) system with a limited sum power budget. We aim to improve the analytical problem to minimize the gap of the DP-OTA-FedAvg coefficient to minimize privacy functions.
  arXiv  Detail & Related papers  (2023-05-05T13:56:40Z)
• Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Framework [68.8725783112254]
  We address the problem of designing incentive mechanisms by a virtual service provider (VSP) to hire sensing IoT devices to sell their sensing data. Due to the limited bandwidth, we propose to use semantic extraction algorithms to reduce the delivered data by the sensing IoT devices. We propose a novel iterative contract design and use a new variant of multi-agent reinforcement learning (MARL) to solve the modelled multi-dimensional contract problem.
  arXiv  Detail & Related papers  (2023-02-22T15:52:37Z)
• Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy [51.11280118806893]
  We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability. We study the local differential privacy guarantees of discrete-valued mechanisms with finite output space through the lens of $f$-differential privacy (DP) More specifically, we advance the existing literature by deriving tight $f$-DP guarantees for a variety of discrete-valued mechanisms.
  arXiv  Detail & Related papers  (2023-02-19T16:58:53Z)
• PP-MARL: Efficient Privacy-Preserving MARL for Cooperative Intelligence in Communication [18.70947104724304]
  We propose PP-MARL, an efficient privacy-preserving learning scheme based on multi-agent reinforcement learning (MARL) We apply and evaluate our scheme in two communication-related use cases: mobility management in drone-assisted communication and network control with edge intelligence.
  arXiv  Detail & Related papers  (2022-04-26T04:08:27Z)
• Secure Distributed/Federated Learning: Prediction-Privacy Trade-Off for Multi-Agent System [4.190359509901197]
  In the big data era, performing inference within the distributed and federated learning (DL and FL) frameworks, the central server needs to process a large amount of data. Considering the decentralized computing topology, privacy has become a first-class concern. We study the textitprivacy-aware server to multi-agent assignment problem subject to information processing constraints associated with each agent.
  arXiv  Detail & Related papers  (2022-04-24T19:19:20Z)
• Privacy-Preserving Communication-Efficient Federated Multi-Armed Bandits [17.039484057126337]
  Communication bottleneck and data privacy are two critical concerns in federated multi-armed bandit (MAB) problems. We design the privacy-preserving communication-efficient algorithm in such problems and study the interactions among privacy, communication and learning performance in terms of the regret.
  arXiv  Detail & Related papers  (2021-11-02T12:56:12Z)
• Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching [91.50631418179331]
  A privacy-preserving distributed deep policy gradient (P2D3PG) is proposed to maximize the cache hit rates of devices in the MEC networks. We convert the distributed optimizations into model-free Markov decision process problems and then introduce a privacy-preserving federated learning method for popularity prediction.
  arXiv  Detail & Related papers  (2021-10-20T02:48:27Z)
• Decentralized Deep Learning for Mobile Edge Computing: A Survey on Communication Efficiency and Trustworthiness [1.4180331276028662]
  Decentralized deep learning (DDL) is a promising solution to privacy-preserving data processing for millions of edge smart devices. In this paper, we demonstrate technical fundamentals of DDL for benefiting many walks of society through decentralized learning.
  arXiv  Detail & Related papers  (2021-07-30T04:15:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.