PP-MARL: Efficient Privacy-Preserving MARL for Cooperative Intelligence in Communication

• URL: http://arxiv.org/abs/2204.12064v1
• Date: Tue, 26 Apr 2022 04:08:27 GMT
• Title: PP-MARL: Efficient Privacy-Preserving MARL for Cooperative Intelligence in Communication
• Authors: Tingting Yuan, Hwei-Ming Chung, Xiaoming Fu
• Abstract summary: 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.
• Score: 18.70947104724304
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial intelligence (AI) has been introduced in communication networks and services to improve efficiency via self-optimization. Cooperative intelligence (CI), also known as collective intelligence and collaborative intelligence, is expected to become an integral element in next-generation networks because it can aggregate the capabilities and intelligence of multiple devices. However, privacy issues may intimidate, obstruct, and hinder the deployment of CI in practice because collaboration heavily relies on data and information sharing. Additional practical constraints in communication (e.g., limited bandwidth) further limit the performance of CI. To overcome these challenges, 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. Simulation results reveal that the proposed scheme can achieve efficient and reliable collaboration with 1.1-6 times better privacy protection and lower overheads (e.g., 84-91% reduction in bandwidth) than state-of-the-art approaches.

Related papers

• Enhancing Physical Layer Communication Security through Generative AI with Mixture of Experts [80.0638227807621]
  generative artificial intelligence (GAI) models have demonstrated superiority over conventional AI methods. MoE, which uses multiple expert models for prediction through a gate mechanism, proposes possible solutions.
  arXiv  Detail & Related papers  (2024-05-07T11:13:17Z)
• Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning [57.652899266553035]
  Decentralized and lifelong-adaptive multi-agent collaborative learning aims to enhance collaboration among multiple agents without a central server. We propose DeLAMA, a decentralized multi-agent lifelong collaborative learning algorithm with dynamic collaboration graphs.
  arXiv  Detail & Related papers  (2024-03-11T09:21:11Z)
• The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
  Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge. This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI) We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
  arXiv  Detail & Related papers  (2024-01-03T07:47:22Z)
• 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)
• Building Cooperative Embodied Agents Modularly with Large Language Models [104.57849816689559]
  We address challenging multi-agent cooperation problems with decentralized control, raw sensory observations, costly communication, and multi-objective tasks instantiated in various embodied environments. We harness the commonsense knowledge, reasoning ability, language comprehension, and text generation prowess of LLMs and seamlessly incorporate them into a cognitive-inspired modular framework. Our experiments on C-WAH and TDW-MAT demonstrate that CoELA driven by GPT-4 can surpass strong planning-based methods and exhibit emergent effective communication.
  arXiv  Detail & Related papers  (2023-07-05T17:59:27Z)
• Killing Two Birds with One Stone: Quantization Achieves Privacy in Distributed Learning [18.824571167583432]
  Communication efficiency and privacy protection are critical issues in distributed machine learning. We propose a comprehensive quantization-based solution that could simultaneously achieve communication efficiency and privacy protection. We theoretically capture the new trade-offs between communication, privacy, and learning performance.
  arXiv  Detail & Related papers  (2023-04-26T13:13:04Z)
• Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning [74.53077322713548]
  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.
  arXiv  Detail & Related papers  (2023-01-26T10:09:23Z)
• 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)
• Learning Individually Inferred Communication for Multi-Agent Cooperation [37.56115000150748]
  We propose Individually Inferred Communication (I2C) to enable agents to learn a prior for agent-agent communication. The prior knowledge is learned via causal inference and realized by a feed-forward neural network. I2C can not only reduce communication overhead but also improve the performance in a variety of multi-agent cooperative scenarios.
  arXiv  Detail & Related papers  (2020-06-11T14:07:57Z)

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.